HUMAN PERCEPTION IN COMPUTER VISION / CONFERENCE SUBMISSIONS
Computer vision has made remarkable progress in recent years. Deep neural network (DNN) models optimized to identify objects in images exhibit unprecedented task-trained accuracy and, remarkably, some generalization ability: new visual problems can now be solved more easily based on previous learning. Biological vision (learned in life and through evolution) is also accurate and generalpurpose. Is it possible that these different learning regimes converge to similar problem-dependent optimal computations? We therefore asked whether the human system-level computation of visual perception has DNN correlates and considered several anecdotal test cases. We found that perceptual sensitivity to image changes has DNN mid-computation correlates, while sensitivity to segmentation, crowding and shape has DNN end-computation correlates. Our results quantify the applicability of using DNN computation to estimate perceptual loss, and are consistent with the fascinating theoretical view that properties of human perception are a consequence of architecture-independent visual learning. 1 QUICK EXPERT SUMMARY Considering the learned computation of ImageNet-trained DNNs, we find: • Large computation changes for perceptually salient image changes (Figure 1). • Gestalt: segmentation, crowding, and shape interactions in computation (Figure 2). • Contrast constancy: bandpass transduction in first layers is later corrected (Figure 3). These properties are reminiscent of human perception, perhaps because learned general-purpose classifiers (human and DNN) tend to converge.
1 QUICK EXPERT SUMMARY
Considering the learned computation of ImageNet-trained DNNs, we find:
• Large computation changes for perceptually salient image changes (Figure 1). • Gestalt: segmentation, crowding, and shape interactions in computation (Figure 2). • Contrast constancy: bandpass transduction in first layers is later corrected (Figure 3).
These properties are reminiscent of human perception, perhaps because learned general-purpose classifiers (human and DNN) tend to converge.
2 INTRODUCTION
Deep neural networks (DNNs) are a class of computer learning algorithms that have become widely used in recent years (LeCun et al., 2015). By training with millions of examples, such models achieve unparalleled degrees of task-trained accuracy (Krizhevsky et al., 2012). This is not unprecedented on its own - steady progress has been made in computer vision for decades, and to some degree current designs are just scaled versions of long-known principles (Lecun et al., 1998). In previous models, however, only the design is general-purpose, while learning is mostly specific to the context of a trained task. Interestingly, for current DNNs trained to solve a large-scale image recognition problem (Russakovsky et al., 2014), the learned computation is useful as a building block for drastically different and untrained visual problems (Huh et al., 2016; Yosinski et al., 2014).
For example, orientation- and frequency-selective features (Gabor patches) can be considered general-purpose visual computations. Such features are routinely discovered by DNNs (Krizhevsky et al., 2012; Zeiler & Fergus, 2013), by other learning algorithms (Hinton & Salakhutdinov, 2006;
∗https://sites.google.com/site/rondekelhomepage/
Lee et al., 2008; 2009; Olshausen & Field, 1997), and are extensively hard-coded in computer vision (Jain & Farrokhnia, 1991). Furthermore, a similar computation is believed to underlie the spatial response properties of visual neurons of diverse animal phyla (Carandini et al., 2005; DeAngelis et al., 1995; Hubel & Wiesel, 1968; Seelig & Jayaraman, 2013), and is evident in human visual perception (Campbell & Robson, 1968; Fogel & Sagi, 1989; Neri et al., 1999). This diversity culminates in satisfying theoretical arguments as to why Gabor-like features are so useful in general-purpose vision (Olshausen, 1996; Olshausen & Field, 1997).
As an extension, general-purpose computations are perhaps of universal use. For example, a dimensionality reduction transformation that optimally preserves recognition-relevant information may constitute an ideal computation for both DNN and animal. More formally, different learning algorithms with different physical implementations may converge to the same computation when similar (or sufficiently general) problems are solved near-optimally. Following this line of reasoning, DNN models with good general-purpose computations may be computationally similar to biological visual systems, even more so than less accurate and less general biologically plausible simulations (Kriegeskorte, 2015; Yamins & DiCarlo, 2016).
Related work seems to be consistent with computation convergence. First, different DNN training regimes seem to converge to a similar learned computation (Li et al., 2015; Zhou et al., 2014). Second, image representation may be similar in trained DNN and in biological visual systems. That is, when the same images are processed by DNN and by humans or monkeys, the final DNN computation stages are strong predictors of human fMRI and monkey electrophysiology data collected from visual areas V4 and IT (Cadieu et al., 2014; Khaligh-Razavi & Kriegeskorte, 2014; Yamins et al., 2014). Furthermore, more accurate DNN models exhibit stronger predictive power (Cadieu et al., 2014; Dubey & Agarwal, 2016; Yamins et al., 2014), and the final DNN computation stage is even a strong predictor of human-perceived shape discrimination (Kubilius et al., 2016). However, some caution is perhaps unavoidable, since measured similarity may be confounded with categorization consistency, view-invariance resilience, or similarity in the inherent difficulty of the tasks undergoing comparison. A complementary approach is to consider images that were produced by optimizing trained DNN-based perceptual metrics (Gatys et al., 2015a;b; Johnson et al., 2016; Ledig et al., 2016), which perhaps yields undeniable evidence of non-trivial computational similarity, although a more objective approach may be warranted.
Here, we quantify the similarity between human visual perception, as measured by psychophysical experiments, and individual computational stages (layers) in feed-forward DNNs trained on a large-scale image recognition problem (ImageNet LSVRC). Comparison is achieved by feeding the experimental image stimuli to the trained DNN and comparing a DNN metric (mean mutual information or mean absolute change) to perceptual data. The use of reduced (simplified and typically non-natural) stimuli ensures identical inherent task difficulty across compared categories and prevents confounding of categorization consistency with measured similarity. Perception, a systemlevel computation, may be influenced less by the architectural discrepancy (biology vs. DNN) than are neural recordings.
3 CORRELATE FOR IMAGE CHANGE SENSITIVITY
From a perceptual perspective, an image change of fixed size has different saliency depending on image context (Polat & Sagi, 1993). To investigate whether the computation in trained DNNs exhibits similar contextual modulation, we used the Local Image Masking Database (Alam et al., 2014), in which 1080 partially-overlapping images were subjected to different levels of the same random additive noise perturbation, and for each image, a psychophysical experiment determined the threshold noise level at which the added-noise image is discriminated from two noiseless copies at 75% (Figure 1a). Threshold is the objective function that is compared with an L1-distance correlate in the DNN representation. The scale of measured threshold was:
20 · log10 (
std (noise) T
) , (1)
where std (noise) is the standard deviation of the additive noise, and T is the mean image pixel value calculated over the region where the noise is added (i.e. image center).
The DNN correlate of perceptual threshold we used was the average L1 change in DNN computation between added-noise images and the original, noiseless image. Formally,
Li,n1 (I) = ∣∣∣ai (I + noise (n))− ai (I)∣∣∣ , (2)
where ai (X) is the activation value of neuron i during the DNN feedforward pass for input image X , and the inner average (denoted by bar) is taken over repetitions with random n-sized noise (noise is introduced at random phase spectra in a fixed image location, an augmentation that follows the between-image randomization described by Alam et al., 2014; the number of repetitions was 10 or more). Unless otherwise specified, the final L1 prediction is L i,n 1 averaged across noise levels (−40 to 25 dB with 5-dB intervals) and computational neurons (first within and then across computational stages). Using L1 averaged across noise levels as a correlate for the noise level of perceptual threshold is a simple approximation with minimal assumptions.
Results show that the L1 metric is correlated with the perceptual threshold for all tested DNN architectures (Figure 1b, 4a-c). In other words, higher values of the L1 metric (indicating larger changes in DNN computation due to image perturbation, consistent with higher perturbation saliency) are
correlated with lower values of measured perceptual threshold (indicating that weaker noise levels are detectable, i.e. higher saliency once more).
To quantify and compare predictive power, we considered the percent of linearly explained variability (R2). For all tested DNN architectures, the prediction explains about 60% of the perceptual variability (Tables 1, 2; baselines at Tables 3-5), where inter-person similarity representing theoretical maximum is 84% (Alam et al., 2014). The DNN prediction is far more accurate than a prediction based on simple image statistical properties (e.g. RMS contrast), and is on par with a detailed perceptual model that relies on dozens of psychophysically collected parameters (Alam et al., 2014). The Spearmann correlation coefficient is much higher compared with the perceptual model (with an absolute SROCC value of about 0.79 compared with 0.70, Table 1), suggesting that the L1 metric gets the order right but not the scale. We did not compare these results with models that fit the experimental data (e.g. Alam et al., 2015; Liu & Allebach, 2016), since the L1 metric has no explicit parameters. Also, different DNN architectures exhibited high similarity in their predictions (R2 of about 0.9, e.g. Figure 4d).
Prediction can also be made from isolated computational stages, instead of across all stages as before. This analysis shows that the predictive power peaks mid-computation across all tested image scales (Figure 1c). This peak is consistent with use of middle DNN layers to optimize perceptual metrics (Gatys et al., 2015a;b; Ledig et al., 2016), and is reminiscent of cases in which low- to mid-level vision is the performance limiting computation in the detection of at-threshold stimuli (Campbell & Robson, 1968; Del Cul et al., 2007).
Finally, considering the images for which the L1-based prediction has a high error suggests a factor which causes a systematic inconsistency with perception (Figures 1d, 6). This factor may be related to the mean image luminance: by introducing noise perturbations according to the scale of Equation 1, a fixed noise size (in dB) corresponds to smaller pixel changes in dark compared with bright images. (Using this scales reflects an assumption of multiplicative rather than additive conservation; this assumption may be justified for the representation at the final but perhaps not the intermediate computational stages considering the log-linear contrast response discussed in Section 5). Another factor may the degree to which image content is identifiable.
4 CORRELATE FOR MODULATION OF SENSITIVITY BY CONTEXT
The previous analysis suggested gross computational similarity between human perception and trained DNNs. Next, we aimed to extend the comparison to more interpretable properties of perception by considering more highly controlled designs. To this end, we considered cases in which a static background context modulates the difficulty of discriminating a foreground shape, despite no spatial overlap of foreground and background. This permits interpretation by considering the cause of the modulation.
We first consider segmentation, in which arrangement is better discriminated for arrays of consistently oriented lines compared with inconsistently oriented lines (Figure 2a) (Pinchuk-Yacobi et al., 2016). Crowding is considered next, where surround clutter that is similar to the discriminated target leads to deteriorated discrimination performance (Figure 2b) (Livne & Sagi, 2007). Last to be addressed is object superiority, in which a target line location is better discriminated when it is in a shape-forming layout (Figure 2c) (Weisstein & Harris, 1974). In this case, clutter is controlled by having the same fixed number of lines in context. To measure perceptual discrimination, these works introduced performance-limiting manipulations such as location jittering, brief presentation, and temporal masking. While different manipulations showed different measured values, order-of-difficulty was typically preserved. Here we changed all the original performance-limiting manipulations to location jittering (whole-shape or element-wise, see Section 8.4).
To quantify discrimination difficulty in DNNs, we measured the target-discriminative information of isolated neurons (where performance is limited by location jittering noise), then averaged across all neurons (first within and then across computational layer stages). Specifically, for each neuron, we measured the reduction in categorization uncertainty due to observation, termed mutual information (MI): MI (Ai;C) = H (C)−H (C|Ai) , (3) where H stands for entropy, and Ai is a random variable for the value of neuron i when the DNN processes a random image from a category defined by the random variable C. For example, if a neuron gives a value in the range of 100.0 to 200.0 when the DNN processes images from category A, and 300.0 to 400.0 for category B, then the category is always known by observing the value, and so mutual information is high (MI=1 bits). On the other extreme, if the neuron has no discriminative task information, then MI=0 bits. To measure MI, we quantized activations into eight equal-amount bins, and used 500 samples (repetitions having different location jittering noise) across categories. The motivation for this correlate is the assumption that the perceptual order-of-difficulty reflects the quantity of task-discriminative information in the representation.
Results show that, across hundreds of configurations (varying pattern element size, target location, jitter magnitude, and DNN architecture; see Section 8.4), the qualitative order of difficulty in terms of the DNN MI metric is consistent with the order of difficulty measured in human psychophysical experiments, for the conditions addressing segmentation and crowding (Figures 2d, 7; for baseline models see Figure 8). It is interesting to note that the increase in similarity develops gradually along different layer types in the DNN computation (i.e. not just pooling layers), and is accompanied by a gradual increase in the quantity of task-relevant information (Figure 2e-g). This indicates a link between task relevance and computational similarity for the tested conditions. Note that unlike the evident increase in isolated unit task information, the task information from all units combined decreases by definition along any computational hierarchy. An intuition for this result is that the total hidden information decreases, while more accessible per-unit information increases.
For shape formation, four out of six shapes consistently show order of difficulty like perception, and two shapes consistently do no (caricature at Figure 2h; actual data at Figure 9).
5 CORRELATE FOR CONTRAST SENSITIVITY
A cornerstone of biological vision research is the use of sine gratings at different frequencies, orientations, and contrasts (Campbell & Robson, 1968). Notable are results showing that the lowest perceivable contrast in human perception depends on frequency. Specifically, high spatial frequencies are attenuated by the optics of the eye, and low spatial frequencies are believed to be attenuated due to processing inefficiencies (Watson & Ahumada, 2008), so that the lowest perceivable contrast is found at intermediate frequencies. (To appreciate this yourself, examine Figure 3a). Thus, for low-contrast gratings, the physical quantity of contrast is not perceived correctly: it is not preserved across spatial frequencies. Interestingly, this is corrected for gratings of higher contrasts, for which perceived contrast is more constant across spatial frequencies (Georgeson & Sullivan, 1975).
The DNN correlate we considered is the mean absolute change in DNN representation between a gray image and sinusoidal gratings, at all combinations of spatial frequency and contrast. Formally, for neurons in a given layer, we measured:
L1(contrast, frequency) = 1
Nneurons Nneurons∑ i=1 ∣∣∣ai (contrast, frequency)− ai (0, 0)∣∣∣ , (4)
where ai (contrast, frequency) is the average activation value of neuron i to 250 sine images (random orientation, random phase), ai (0, 0) is the response to a blank (gray) image, and Nneurons is the number of neurons in the layer. This measure reflects the overall change in response vs. the gray image.
Results show a bandpass response for low-contrast gratings (blue lines strongly modulated by frequency, Figures 3, 10), and what appears to be a mostly constant response at high contrast for end-computation layers (red lines appear more invariant to frequency), in accordance with perception.
We next aimed to compare these results with perception. Data from human experiments is generally iso-output (i.e. for a pre-set output, such as 75% detection accuracy, the input is varied to find the value which produce the preset output). However, the DNN measurements here are iso-input (i.e. for a fixed input contrast the L1 is measured). As such, human data should be compared to the interpoalted inverse of DNN measurements. Specifically, for a set output value, the interpolated contrast value which produce the output is found for every frequency (Figure 11). This analysis permits quantifying the similarity of iso-output curves for human and DNN, measured here as the percent of log-Contrast variability in human measurements which is explained by the DNN predictions. This showed a high explained variability at the end computation stage (prob layer, R2 = 94%), but importantly, a similarly high value at the first computational stage (conv1 1 layer, R2 = 96%). Intiutively, while the ”internal representation” variability in terms of L1 is small, the iso-output number-of-input-contrast-cahnges variability is still high. For example. for the prob layer, about the same L1 is measured for (Contrast=1,freq=75) and for (Contrast=0.18,freq=12).
An interesting, unexpected observation is that the logarithmically spaced contrast inputs are linearly spaced at the end-computation layers. That is, the average change in DNN representation scales logarithmically with the size of input change. This can be quantified by the correlation of output L1 with log Contrast input, which showed R2 = 98% (averaged across spatial frequencies) for prob, while much lower values were observed for early and middle layers (up to layer fc7). The same computation when scrambling the learned parameters of the model showed R2 = 60%. Because the degree of log-linearity observed was extremely high, it may be an important emergent property of the learned DNN computation, which may deserve further investigation. However, this property is only reminiscent and not immediately consistent with the perceptual power-law scaling (Gottesman et al., 1981).
6 DISCUSSION
6.1 HUMAN PERCEPTION IN COMPUTER VISION
It may be tempting to believe that what we see is the result of a simple transformation of visual input. Centuries of psychophysics have, however, revealed complex properties in perception, by crafting stimuli that isolate different perceptual properties. In our study, we used the same stimuli to investigate the learned properties of deep neural networks (DNNs), which are the leading computer vision algorithms to date (LeCun et al., 2015).
The DNNs we used were trained in a supervised fashion to assign labels to input images. To some degree, this task resembles the simple verbal explanations given to children by their parents. Since human perception is obviously much richer than the simple external supervision provided, we were not surprised to find that the best correlate for perceptual saliency of image changes is a part of the DNN computation that is only supervised indirectly (i.e. the mid-computation stage). This similarity is so strong, that even with no fine-tuning to human perception, the DNN metric is competitively accurate, even compared with a direct model of perception.
This strong, quantifiable similarity to a gross aspect of perception may, however, reflect a mix of similarities and discrepancies in different perceptual properties. To address isolated perceptual effects, we considered experiments that manipulate a spatial interaction, where the difficulty of discriminating a foreground target is modulated by a background context. Results showed modulation of DNN target diagnostic, isolated unit information, consistent with the modulation found in perceptual discrimination. This was shown for contextual interactions reflecting grouping/segmentation (Harris et al., 2015), crowding/clutter (Livne & Sagi, 2007; Pelli et al., 2004), and shape superiority (Weisstein & Harris, 1974). DNN similarity to these groupings/gestalt phenomena appeared at the end-computation stages.
No less interesting, are the cases in which there is no similarity. For example, perceptual effects related to 3D (Erdogan & Jacobs, 2016) and symmetry (Pramod & Arun, 2016) do not appear to have a strong correlate in the DNN computation. Indeed, it may be interesting to investigate the influence of visual experience in these cases. And, equally important, similarity should be considered in terms of specific perceptual properties rather than as a general statement.
6.2 RECURRENT VS. FEEDFORWARD CONNECTIVITY
In the human hierarchy of visual processing areas, information is believed to be processed in a feedforward sweep, followed by recurrent processing loops (top-down and lateral) (Lamme & Roelfsema, 2000). Thus, for example, the early visual areas can perform deep computations. Since mapping from visual areas to DNN computational layers is not simple, it will not be considered here. (Note that ResNet connectivity is perhaps reminiscent of unrolled recurrent processing).
Interestingly, debate is ongoing about the degree to which visual perception is dependent on recurrent connectivity (Fabre-Thorpe et al., 1998; Hung et al., 2005): recurrent representations are obviously richer, but feedforward computations converge much faster. An implicit question here regarding the extent of feasible feed-forward representations is, perhaps: Can contour segmentation, contextual influences, and complex shapes be learned? Based on the results reported here for feedforward DNNs, a feedforward representation may seem sufficient. However, the extent to which this is true may be very limited. In this study we used small images with a small number of lines, while effects such as contour integration seem to take place even in very large configurations (Field et al., 1993). Such scaling seems more likely in a recurrent implementation. As such, a reasonable hypothesis may be that the full extent of contextual influence is only realizable with recurrence, while feedforward DNNs learn a limited version by converging towards a useful computation.
6.3 IMPLICATIONS AND FUTURE WORK
6.3.1 USE IN BRAIN MODELING
The use of DNNs in modeling of visual perception (or of biological visual systems in general) is subject to a tradeoff between accuracy and biological plausibility. In terms of architecture, other deep models better approximate our current understanding of the visual system (Riesenhuber &
Poggio, 1999; Serre, 2014). However, the computation in trained DNN models is quite generalpurpose (Huh et al., 2016; Yosinski et al., 2014) and offers unparalleled accuracy in recognition tasks (LeCun et al., 2015). Since visual computations are, to some degree, task- rather than architecturedependent, an accurate and general-purpose DNN model may better resemble biological processing than less accurate biologically plausible ones (Kriegeskorte, 2015; Yamins & DiCarlo, 2016). We support this view by considering a controlled condition in which similarity is not confounded with task difficulty or categorization consistency.
6.3.2 USE IN PSYCHOPHYSICS
Our results imply that trained DNN models have good predictive value for outcomes of psychophysical experiments, permitting a zero-cost first-order approximation. Note, however, that the scope of such simulations may be limited, since learning (Sagi, 2011) and adaptation (Webster, 2011) were not considered here.
Another fascinating option is the formation of hypotheses in terms of mathematically differentiable trained-DNN constraints, whereby it is possible to efficiently solve for the visual stimuli that optimally dissociate the hypotheses (see Gatys et al. 2015a;b; Mordvintsev et al. 2015 and note Goodfellow et al. 2014; Szegedy et al. 2013). The conclusions drawn from such stimuli can be independent of the theoretical assumptions about the generating process (for example, creating new visual illusions that can be seen regardless of how they were created).
6.3.3 USE IN ENGINEERING (A PERCEPTUAL LOSS METRIC)
As proposed previously (Dosovitskiy & Brox, 2016; Johnson et al., 2016; Ledig et al., 2016), the saliency of small image changes can be estimated as the representational distance in trained DNNs. Here, we quantified this approach by relying on data from a controlled psychophysical experiment (Alam et al., 2014). We found the metric to be far superior to simple image statistical properties, and on par with a detailed perceptual model (Alam et al., 2014). This metric can be useful in image compression, whereby optimizing degradation across image sub-patches by comparing perceptual loss may minimize visual artifacts and content loss.
ACKNOWLEDGMENTS
We thank Yoram Bonneh for his valuable questions which led to much of this work.
7 APPENDIX: FIGURES AND TABLES
8 APPENDIX: EXPERIMENTAL SETUP
8.1 DNN MODELS
To collect DNN computation snapshots, we used MATLAB with MatConvNet version 1.0-beta20 (Vedaldi & Lenc, 2015). All MATLAB code will be made available upon acceptance of this manuscript. The pre-trained DNN models we have used are: CaffeNet (which is a variant of AlexNet provided in Caffe, Jia et al., 2014), GoogLeNet (Szegedy et al., 2014), VGG-19 (Simonyan & Zisserman, 2014), and ResNet-152 (He et al., 2015). The models were trained on the same ImageNet LSVRC. The CaffeNet model was trained using Caffe with the default ImageNet training parameters (stopping at iteration 310, 000) and imported into MatConvNet. For the GoogLeNet model, we used the imported pre-trained reference-Caffe implementation. For VGG-19 and ResNet-152, we used the imported pre-trained original versions. In all experiments input image size was 224× 224 or 227× 227.
8.2 BASELINE MODELS
As baselines to compare with pre-trained DNN models, we consider: (a) a multiscale linear filter bank of Gabor functions, (b) a steerable-pyramid linear filter bank (Simoncelli & Freeman, 1995), (c) the VGG-19 model for which the learned parameters (weights) were randomly scrambled within layer, and (d) the CaffeNet model at multiple time points during training. For the Gabor decomposition, the following Gabor filters were used: all compositions of σ = {1, 2, 4, 8, 16, 32, 64}px, λ = {1, 2} · σ, orientation= {0, π/3, 2π/3, π, 4π/3, 5π/3}, and phase= {0, π/2}.
8.3 IMAGE PERTURBATION EXPERIMENT
The noiseless images were obtained from Alam et al. (2014). In main text, ”image scale” refers to percent coverage of DNN input. Since size of original images (149 × 149) is smaller than DNN input of (224× 224) or (227× 227), the images were resized by a factor of 1.5 so that 100% image scale covers approximately the entire DNN input area.
Human psychophysics and DNN experiments were done for nearly identical images. A slight discrepancy relates to how the image is blended with the background in the special case where the region where noise is added has no image surround at one or two side. In these sides (which depend on the technical procedure with which images were obtained, see Alam et al., 2014), the surround blending here was hard, while the original was smooth.
8.4 BACKGROUND CONTEXT EXPERIMENT
8.4.1 SEGMENTATION
The images used are based on the Texture Discrimination Task (Karni & Sagi, 1991). In the variant considered here (Pinchuk-Yacobi et al., 2015), subjects were presented with a grid of lines, all of which were horizontal, except two or three that were diagonal. Subjects discriminated whether the arrangement of diagonal lines is horizontal or vertical, and this discrimination was found to be more difficult when the central line is horizontal rather than diagonal (”Hard” vs. ”Easy” in Figure 2a). To limit human performance in this task, two manipulations were applied: (a) the location of each line in the pattern was jittered, and (b) a noise mask was presented briefly after the pattern. Here we only retained (a).
A total of 90 configurations were tested, obtained by combinations of the following alternatives:
• Three scales: line length of 9, 12.3, or 19.4 px (number of lines co-varied with line length, see Figure 12).
• Three levels of location jittering, defined as a multiple of line length: {1, 2, 3} · 0.0625 · l px, where l is the length of a line in the pattern. Jittering was applied separately to each line in the pattern.
• Ten locations of diagonal lines: center, random, four locations of half-distance from center to corners, four locations of half-distance from center to image borders.
For each configuration, the discriminated arrangement of diagonal lines was either horizontal or vertical, and the central line was either horizontal or diagonal (i.e. hard or easy).
8.4.2 CROWDING
The images used are motivated by the crowding effect (Livne & Sagi, 2007; Pelli et al., 2004).
A total of 90 configurations were tested, obtained by combinations of the following alternatives:
• Three scales: font size of 15.1, 20.6, or 32.4 px (see Figure 13). • Three levels of discriminated-letter location jittering, defined as a multiple of font size: {1, 2, 3} · 0.0625 · l px, where l is font size. The jitter of surround letters (M, N, S, and T) was fixed (i.e. the background was static).
• Ten locations: center, random, four locations of half-distance from center to corners, four locations of half-distance from center to image borders.
For each configuration, the discriminated letter was either A, B, C, D, E, or F, and the background was either blank (easy) or composed of the letters M, N, S, and T (hard).
8.4.3 SHAPE
The images used are based on the object superiority effect by Weisstein & Harris (1974), where discriminating a line location is easier when combined with surrounding lines a shape is formed.
A total of 90 configurations were tested, obtained by combinations of the following alternatives:
• Three scales: discriminated-line length of 9, 15.1, or 22.7 px (see Figure 14). • Five levels of whole-pattern location jittering, defined as a multiple of discriminated-line
length: {1, 2, 5, 10, 15} · 0.0625 · l px, where l is the length of the discriminated line.
• Six ”hard” background line layouts (patterns b-f of their Figure 2 and the additional pattern f of their Figure 3 in Weisstein & Harris, 1974). The ”easy” layout was always the same (pattern a).
For each configuration, the line whose location is discriminated had four possible locations (two locations are shown in Figure 2c), and the surrounding background line layout could compose a shape (easy) or not (hard).
8.5 CONTRAST SENSITIVITY EXPERIMENT
Used images depicted sine gratings at different contrast, spatial frequency, sine phase, and sine orientation combinations.
The author works to compare DNNs to human visual perception, both quantitatively and qualitatively. 
Their first result involves performing a psychophysical experiment both on humans and on a model and then comparing the results (actually I think the psychophysical data was collected in a different work, and is just used here).   The specific psychophysical experiment determined, separately for each of a set of approx. 1110 images, what the noise level of additive noise would have to be to make a just-noticeable-difference for humans in discriminating the noiseless image from the noisy one.   The authors then define a metric on neural networks that allows them to measure what they posit might be a similar property for the networks.  They then correlate the pattern of noise levels between neural networks that the humans.    Deep neural networks end up being much better predictors of the human pattern of noise levels than simpler measure of image perturbation (e.g. RMS contrast).  
A second result involves comparing DNNs to humans in terms of their pattern errors in a series of highly controlled experiments using stimuli that illustrate classic properties of human visual processing -- including segmentation, crowding and shape understanding.  They then used an information-theoretic single-neuron metric of discriminability to assess similar patterns of errors for the DNNs.   Again, top layers of DNNs were able to reproduce the human patterns of difficulty across stimuli, at least to some extent. 
A third result involves comparing DNNs to humans in terms of their pattern of contrast sensitivity across a series of sine-grating images at different frequencies.  (There is a classic result from vision research as to what this pattern should be, so it makes a natural target for comparison to models.)   The authors define a DNN correlate for the propertie in terms of the cross-neuron average of the L1-distance between responses to a blank image and responses to a sinuisoid of each contrast and frequency.   They then qualitatively compare the results of this metric for DNNs models to known results from the literature on humans, finding that, like humans, there is an apparent bandpass response for low-contrast gratings and a mostly constant response at high contrast.  
Pros:
    * The general concept of comparing deep nets to psychophysical results in a detailed, quantitative way, is really nice.   
    * They nicely defined a set of "linking functions", e.g. metrics that express how a specific behavioral result is to be generated from the neural network.  (Ie. the L1 metrics in results 1 and 3 and the information-theoretic measure in result 2.)   The framework for setting up such linking functions seems like a great direction to me. 
    * The actual psychophysical data seems to have been handled in a very careful and thoughtful way.   These folks clearly know what they're doing on the psychophysical end.  
Cons:
    * To my mind, the biggest problem wit this paper is that that it doesn't say something that we didn't really know already.   Existing results have shown that DNNs are pretty good models of the human visual system in a whole bunch of ways, and this paper adds some more ways.    What would have been great would be: 
         (a) showing that they metric of comparison to humans that was sufficiently sensitive that it could pull apart various DNN models, making one clearly better than the others. 
         (b) identifying a wide gap between the DNNs and the humans that is still unfilled.   They sort of do this, since while the DNNs are good at reproducing the human judgements in Result 1, they are not perfect -- gap is between 60% explained variance and 84% inter-human consistency.    This 24% gap is potentially important, so I'd really like to see them have explored that gap more -- e.g. (i) widening the gap by identifying which images caused the gap most and focusing a test on those, or (ii) closing the gap by training a neural network to get the pattern 100% correct and seeing if that made better CNNs as measured on other metrics/tasks. 
In other words, I would definitely have traded off not having results 2 and 3 for a deeper exploration of result 1.    I think their overall approach could be very fruitful, but it hasn't really been carried far enough here. 
   * I found a few things confusing about the layout of the paper.  I especially found that the quantitative results for results 2 and 3 were not clearly displayed.   Why was figure 8 relegated to the appendix?  Where are the quantifications of model-human similarities for the data shown in Figure 8?  Isn't this the whole meat of their second result?   This should really be presented in a more clear way.    
    * Where is the quantification of model-human similarity for the data show in Figure 3?  Isn't there a way to get the human contrast-sensitivity curve and then compare it to that of models in a more quantitively precise way, rather than just note a qualitative agreement?   It seems odd to me that this wasn't done.