Eigen-Distortions of Hierarchical Representations
We develop a method for comparing hierarchical image representations in terms of their ability to explain perceptual sensitivity in humans. Specifically, we utilize Fisher information to establish a model-derived prediction of sensitivity to local perturbations of an image. For a given image, we compute the eigenvectors of the Fisher information matrix with largest and smallest eigenvalues, corresponding to the model-predicted most-and least-noticeable image distortions, respectively. For human subjects, we then measure the amount of each distortion that can be reliably detected when added to the image. We use this method to test the ability of a variety of representations to mimic human perceptual sensitivity. We find that the early layers of VGG16, a deep neural network optimized for object recognition, provide a better match to human perception than later layers, and a better match than a 4-stage convolutional neural network (CNN) trained on a database of human ratings of distorted image quality. On the other hand, we find that simple models of early visual processing, incorporating one or more stages of local gain control, trained on the same database of distortion ratings, provide substantially better predictions of human sensitivity than either the CNN, or any combination of layers of VGG16.
We develop a method for comparing hierarchical image representations in terms of their ability to explain perceptual sensitivity in humans. Specifically, we utilize Fisher information to establish a model-derived prediction of sensitivity to local perturbations of an image. For a given image, we compute the eigenvectors of the Fisher information matrix with largest and smallest eigenvalues, corresponding to the model-predicted most-and least-noticeable image distortions, respectively. For human subjects, we then measure the amount of each distortion that can be reliably detected when added to the image. We use this method to test the ability of a variety of representations to mimic human perceptual sensitivity. We find that the early layers of VGG16, a deep neural network optimized for object recognition, provide a better match to human perception than later layers, and a better match than a 4-stage convolutional neural network (CNN) trained on a database of human ratings of distorted image quality. On the other hand, we find that simple models of early visual processing, incorporating one or more stages of local gain control, trained on the same database of distortion ratings, provide substantially better predictions of human sensitivity than either the CNN, or any combination of layers of VGG16.
Human capabilities for recognizing complex visual patterns are believed to arise through a cascade of transformations, implemented by neurons in successive stages in the visual system. Several recent studies have suggested that representations of deep convolutional neural networks trained for object recognition can predict activity in areas of the primate ventral visual stream better than models constructed explicitly for that purpose (Yamins et al. [2014], Khaligh-Razavi and Kriegeskorte [2014]). These results have inspired exploration of deep networks trained on object recognition as models of human perception, explicitly employing their representations as perceptual distortion metrics or loss functions (Hénaff and Simoncelli [2016], Johnson et al. [2016], Dosovitskiy and Brox [2016]).
On the other hand, several other studies have used synthesis techniques to generate images that indicate a profound mismatch between the sensitivity of these networks and that of human observers. Specifically, Szegedy et al. [2013] constructed image distortions, imperceptible to humans, that cause their networks to grossly misclassify objects. Similarly, Nguyen and Clune [2015] optimized randomly initialized images to achieve reliable recognition by a network, but found that the resulting 'fooling images' were uninterpretable by human viewers. Simpler networks, designed for texture classification and constrained to mimic the early visual system, do not exhibit such failures (Portilla and Simoncelli [2000]). These results have prompted efforts to understand why generalization failures of this type are so consistent across deep network architectures, and to develop more robust training methods to defend networks against attacks designed to exploit these weaknesses (Goodfellow et al. [2014]).
From the perspective of modeling human perception, these synthesis failures suggest that representational spaces within deep neural networks deviate significantly from those of humans, and that methods for comparing representational similarity, based on fixed object classes and discrete sampling of the representational space, are insufficient to expose these deviations. If we are going to use such networks as models for human perception, we need better methods of comparing model representations to human vision. Recent work has taken the first step in this direction, by analyzing deep networks' robustness to visual distortions on classification tasks, as well as the similarity of classification errors that humans and deep networks make in the presence of the same kind of distortion (Dodge and Karam [2017]).
Here, we aim to accomplish something in the same spirit, but rather than testing on a set of handselected examples, we develop a model-constrained synthesis method for generating targeted test stimuli that can be used to compare the layer-wise representational sensitivity of a model to human perceptual sensitivity. Utilizing Fisher information, we isolate the model-predicted most and least noticeable changes to an image. We test these predictions by determining how well human observers can discriminate these same changes. We apply this method to six layers of VGG16 (Simonyan and Zisserman [2015]), a deep convolutional neural network (CNN) trained to classify objects. We also apply the method to several models explicitly trained to predict human sensitivity to image distortions, including both a 4-stage generic CNN, an optimally-weighted version of VGG16, and a family of highly-structured models explicitly constructed to mimic the physiology of the early human visual system. Example images from the paper, as well as additional examples, are available at http://www.cns.nyu.edu/~lcv/eigendistortions/.
PAPER SUMMARY
The paper attempts to measure the degree to which learned image representations from convolutional networks can explain human perceptual sensitivity to visual stimuli. This question is well-motivated on the neuroscience side by recent studies that have used learned image representations to predict brain activity in primates, and on the computer vision side by recent methods for image synthesis which have used image representations learned for object recognition as a surrogate for human perception.
The paper develops a clever experimental paradigm to test the extent to which a (differentiable) image representation agrees with human perception by experimentally measuring the human discrimination threshold for image distortions along eigendirections of the Fisher information matrix corresponding to extremal eigenvalues.
Using this experimental technique, the paper demonstrates that for a VGG-16 model pretrained for ImageNet classification, image representations at earlier layers correlate with human perception more strongly than those at later layers.
The paper next explores image representations from models trained explicitly to match human judgements of perceived image distortion, training both a four-layer CNN and an On-Off model whose structure reflects the structure of the human LGN. Both models are able to fit the training data well, with the CNN slightly outperforming the On-Off model. However using the experimental paradigm from above, the paper shows that image representations from the On-Off model correlate much more strongly with human perception than either the shallow CNN model or representations from any layer of the VGG-16 model trained on ImageNet.
FEATURES FROM IQA CNN
For the four-layer IQA CNN from Section 3.1, it seems that only the image representation from the final layer of the network were compared with human perceptual judgements; however from Figure 3 we know that earlier layers of VGG-16 explain human perceptual judgements much better than later layers. I’m therefore curious about whether features from earlier layers of the IQA CNN would do a better job of explaining human perception than final layer features.
RANDOM NETWORKS AND GANS
Some very recent work has shown that features from shallow CNNs with no pooling and random filters can be used for texture synthesis nearly as well as features from deep networks pretrained on ImageNet (Ustyuzhaninov et al, ICLR 2017), suggesting that the network structure itself and not the task on which it was trained may be a key factor in determining the types of perceptual information its features capture. I wonder whether the experimental paradigm of this paper could be used to probe the perceptual effects of trained vs random networks?
In a similar vein, I wonder whether features from the discriminator of a GAN would better match human perception than ImageNet models, since such a discriminator is explicitly trained to detect synthetic images?
ON-OFF MODEL FOR IMAGE SYNTHESIS
Given the relative success of the On-Off model at explaining human perceptual judgements, I wonder whether it could be used in place of VGG features for image synthesis tasks such as super-resolution or texture generation?
OVERALL
Overall I believe this to be a high-quality, well-written paper with an interesting technical approach and exciting experimental results which open up many interesting questions to be explored in future work.
POST AUTHOR RESPONSE
I did not have any major issues with the paper as submitted, and I thank the authors for entertaining some of my suggestions about potential future work. To me this paper is a clear accept.