MINIMAL IMAGES IN DEEP NEURAL NETWORKS: FRAGILE OBJECT RECOGNITION IN NATURAL IMAGES
The human ability to recognize objects is impaired when the object is not shown in full. "Minimal images" are the smallest regions of an image that remain recognizable for humans. Ullman et al. (2016) show that a slight modification of the location and size of the visible region of the minimal image produces a sharp drop in human recognition accuracy. In this paper, we demonstrate that such drops in accuracy due to changes of the visible region are a common phenomenon between humans and existing state-of-the-art deep neural networks (DNNs), and are much more prominent in DNNs. We found many cases where DNNs classified one region correctly and the other incorrectly, though they only differed by one row or column of pixels, and were often bigger than the average human minimal image size. We show that this phenomenon is independent from previous works that have reported lack of invariance to minor modifications in object location in DNNs. Our results thus reveal a new failure mode of DNNs that also affects humans to a much lesser degree. They expose how fragile DNN recognition ability is for natural images even without adversarial patterns being introduced. Bringing the robustness of DNNs in natural images to the human level remains an open challenge for the community.
Thanks the authors for an interesting work!
The paper studies the differences between human and DNN vision via means of minimal images (i.e. smallest image crops that can be correctly classified).
There are a few notable take-away messages:
1. DNNs are not invariant to even tiny (1-2 px) translations of small image crops.
    - It would be more insightful if authors added comparison between DNN sensitivity to tiny translations of small image crops vs. full-size images (i.e. translation-based adversarial examples https://openreview.net/forum?id=BJfvknCqFQ ).
2. The smaller the image crops, the more sensitive DNNs become (here, more FRIs)
3. DNNs and human vision misclassify the image crops differently: (1) DNNs have almost twice more FRI(s) and (2) FRIs of human and DNNs differ in location.
Questions:
- "After extracting the region from the image, the region is resized to be of the size required by the network."
Would upscaling say a small 28x28 crop into 224x224 image here naturally negatively impact the DNN predictive performance?
That is, because typically image classifiers are trained on one (or a few) fixed resolution(s) of images.
One hypothesis here is that fragile recognition may be because the test image resolution does not match the training image resolution.
Human on the other hands, have been trained on images of variable resolutions.
An alternative to upscaling here is to zero-pad the crop region. Can you help us understand your choice of upscaling here?
+ Originality
The originality is limited as it is a close extenstion work of Ullman et al. 2016
+ Clarity
The paper is well written and presented.
+ Significance
This work extends our understanding of the differences between DNNs and human vision.
However, given what we learned from the adversarial example research area, the contribution of this work is low because results might not be too surprising.