Summary and Contributions: This paper presents a model to explain visual illusions based on considering patch likelihood. This presents a novel take on the impetus for a certain set of illusions and is an interesting casting of the problem.
Strengths: The model appears to be successful in reproducing several phenomena that give rise to visual illusions based on patch likelihood. The images corresponding to visual illusions that are generated are interesting. The significance and novelty of the paper seem relatively strong although I am not an expert in this area. This is clearly relevant to the NeurIPS community.
Weaknesses: The assumption that patch likelihood is appropriately measured could use some more justification. Additionally, there could be more examples of similar phenomena explained by the model.
Correctness: Claims and methods in the paper appear to be correct. Empirical methodology also appears to be well done.
Clarity: The paper is clear and well written.
Relation to Prior Work: To the best of my knowledge, the coverage of previous contributions is adequate.
Additional Feedback: In light of all the additional context, rebuttal and discussion I am inclined to stand by my original score for this paper.
Summary and Contributions: # POST REBUTTAL I have decided to keep my score as it is - the authors have offered clarifications for the concerns and questions I had. I think is very interesting work and hope to see it get in the conference. # This paper presents a framework for explaining (some) visual illusions using patch statistics. A patch generative model is learned which can estimate the likelihood of a given patch. It is shown that under several controlled settings the percentile rank of the patch is a good indicator to which interpretation the ROI in the patch would be perceived. Furthermore it is shown that by modifying the likelihood of patches in a masked area in an image and regenerating them it is possible to create some simple illusions in a natural image.
Strengths: I think this is a very nice paper which has several strengths: * The paper and general approach are interesting * The method is demonstrated to work well on three illusions which share some inherent properties, but are different enough to make a convincing case. * It is shown that to some extent an illusory image can be generated by modifying the likelihood of patches - this requires a bit of careful masking in my eyes, but still neat. * An actually interesting and relevant use for generative models which goes beyond generating nice images of faces or dogs.
Weaknesses: There are several issues here which I would like the authors to address: * Could the authors comment on the use percentile rank? I understand the reasoning behind it more or less but this is not explained in the paper at all. * What is the relationship between the CDF and percentile rank in this case? is there a way to express one with the other? * The experiments show that in a controlled setting (where a clear target patch and template patch are defined) it is possible to explain several illusions. One thing which is common to all the illusions is that the target patch is flat - what about cases where the patch to explain may have some structure? like the Kanitze triangle? this would make a much more convincing case for the method. * The authors show that the percentile rank correlates with the perceived *relative* lightness (for example) but they do not show if this is actually at the same scale of perception - do subjects report the same change in lightness perception? (I'm sure these numbers can be found in literature). * Only one generative model is tested here - do results change with other models? say a simple GMM or a sparse coding based one?
Correctness: Seems so.
Clarity: The paper is nicely presented and clearly written.
Relation to Prior Work: Seems good.
Additional Feedback: More information about the actual model implementation and networks used would be useful.
Summary and Contributions: The authors use current generative models based on invertible normalizing flows to propose an interesting update of classical statistical ideas from the visual neuroscience community to explain visual illusions occurring in biological vision systems. -- Post Rebuttal --: I've kept my positive score and am happy with the rebuttal sent by the authors addressing some of my concerns. I encourage the authors to include the suggested citations
Strengths: The strength of the paper is the proposition of a unifed approach for explaining visual illusions and generating them. Visual illusions are interpreted according to the departure of images from the expected behavior. Relation between a physical magnitude (e.g. luminance or chromatic purity) and the corresponding perceptual magnitude (lightness or saturation) is given by the cumulative density function of natural images along the physical dimension. The likelihood of image patches is computed from an invertible flow presented at NeurIPS-2018. The invertibility of the flow allows to synthesize new images with the desired likelihood, leading to changes in the perception. The use of this recent generative model makes conclusions solid and allows systematic evaluation of the claims. The proposed explanation of illusions may clarify the statistical principles that shape the behavior of the visual brain. Unveiling these principles is a fundamental goal of the Neural Information Processing community.
Weaknesses: The only weakness of the work is on the relation with previous literature: statistical explanations of illusions were suggested before Purves et al., and alternative uniformization and Gaussianization techniques have been proposed to implement these ideas. See the specific connections below in the "relation to prior work" question.
Correctness: There is no technical problem in the proposed methodology. Actually, the use of normalizing flows to explore the relation between the biological behavior and the statistics of visual input is practical and promising.
Clarity: Methodology is properly explained and the description allows the reader to train the flow and compute the corresponding probabilities. The synthesis of new images from the inverse of the flow should stress that one should select solutions along the considered physical dimension (e.g. luminance or chromatic purity). This constraint should be more crearly stated because if not (as far as I understood) a target probability may be obtained by very different images/contexts.
Relation to Prior Work: On the one hand, the current text is too focused on the contributions of Purves et al. Fantastic papers of Purves et al. are very inspiring, but similar ideas were suggested before and this has to be acknowledged. Specifically, Horace Barlow proposed that matching the sensors to the statistics of the stimuli in order to reduce redundancy in the response (using a sort of linear ICA) could lead to visual illusions [Barlow90]. More generally, redundancy reduction or information maximization is connected to (nonlinear) Gaussianization and uniformization transforms. Therefore, more recent uniformization techniques such as Sequential Principal Curves Analysis (SPCA) have been proposed to explain the emergence of illusions when environment is changed [Lapàrra15]. Nonlinear transforms for error minimization [Twer01,McLeod03] may also be achieved by SPCA, thus giving alternative statistical explanation for illusions [Laparra15]. Moreover, error minimization also explains similitudes between visual illusions in artificial neural networks and human viewers [Gomez-Villa20]. On the other hand, regarding invertible flows, the selected Glow transform is very similar to previous invertible Gaussianization transforms such as [Laparra11]. That rotation-based Gaussianization also identifies Gaussian latent spaces, it is able to compute the likelihood of individual patches and it is invertible so that the proposed methodology could also be implemented with [Laparra11]. [Barlow90] Barlow, H. (1990). “A theory about the functional role and synaptic mechanism of visual aftereffects,” in Vision: Coding and Efficiency, ed C. B. Blakemore (Cambridge, UK: Cambridge University Press), 363–375 [Laparra15] Laparra V and Malo J (2015) Visual aftereffects and sensory nonlinearities from a single statistical framework. Front. Hum. Neurosci. 9:557. doi: 10.3389/fnhum.2015.00557 [Twer01] Twer, T., and MacLeod, D. A. (2001). Optimal nonlinear codes for the perception of natural colours. Network 12, 395–407. doi: 10.1080/net.12.3.395.407 [McLeod03] MacLeod, D. A. (2003). “Colour discrimination, colour constancy, and natural scene statistics,” in Normal and Defective Colour Vision, eds J. Mollon, J. Pokorny, and K. Knoblauch (Oxford, UK: Oxford University Press), 189–218. [Gomez-Villa20] Gomez-Villa A, Martin A, Vazquez J, Bertalmio M, and Malo J. Color Illusions Also Deceive CNNs for Low-Level Vision Tasks: Analysis and Implications Accepted in Vision Research. https://arxiv.org/abs/1912.01643 [Laparra11] Laparra, V., Camps, G., and Malo, J. (2011). Iterative gaussianization: from ICA to random rotations. IEEE Trans. Neural Netw. 22, 537–549. doi: 10.1109/TNN.2011.2106511
Additional Feedback: I think authors should include the suggested references to clarify (1) previous statistical explanations of visual illusions based on infomax and error minimization, and (2) the relation with previous invertible Gaussianization transforms.
Summary and Contributions: The paper proposes a statistical theory and associated model to explain contrast-type visual illusions. In a nutshell, the authors train a flow model on the Places dataset, which can then be used to gauge the likelihood in the world of any 16x16 image patch from a test image. After the model is trained, a new illusion is segmented into a target region (which will be misperceived by humans) and a template region (which provides context that is believed to drive the misperception). The paper focuses on contrast-type illusions (over intensity, saturation, and hue), where a central patch's perception is influenced by its surroundings. Interestingly, the method is shown to be useful for generating new illusions.
Strengths: - interesting quantitative approach to analyzing illusions. - well written paper with engaging insights.
Weaknesses: - The authors propose to create a "single unified method can explain a variety of illusions" (line 70), but this is an overstatement. Only 3 types of illusions are studied (simultaneous contrast, White's, Hermann grid), which are actually quite similar within the overall very broad realm of visual illusions (which includes other dimensions like size constancy, collinearity/offset illusions, illusory contours/shapes, various 3D illusions like the Penrose stairs, dynamic illusions like the barber pole, breathing square, and many others, etc). - Thus, while the approach is interesting, its scope is quite narrow. It would help rather than hurt if the authors made that clearer (e.g., in the title, abstract, and contributions).
Correctness: The results are interesting and the approach appears correct, within its relatively narrow scope.
Clarity: The paper is well written but should clarify the breadth of scope better.
Relation to Prior Work: Good review of previous work.
Additional Feedback: An interesting approach, yet it left this reviewer in search for a more general theory of visual illusions, which would reach beyond contrast-type illusions.