Deep Neural Networks as a Computational Model for the  Human Perception of Visual Symmetry

AbstractDeep neural networks have revolutionized computer vision, and their object representations across layers match coarsely with visual cortical areas in the brain. However, whether these representations exhibit qualitative patterns seen in human perception or brain representations remains unresolved. Here, we recast well-known perceptual and neural phenomena in terms of distance comparisons, and ask whether they are present in feedforward deep neural networks trained for object recognition. Some phenomena were present in randomly initialized networks, such as the global advantage effect, sparseness, and relative size. Many others were present after object recognition training, such as the Thatcher effect, mirror confusion, Weber’s law, relative size, multiple object normalization and correlated sparseness. Yet other phenomena were absent in trained networks, such as 3D shape processing, surface invariance, occlusion, natural parts and the global advantage. These findings indicate sufficient conditions for the emergence of these phenomena in brains and deep networks, and offer clues to the properties that could be incorporated to improve deep networks.

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Controversial stimuli: Pitting neural networks against each other as models of human cognition

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1912334117 ◽

2020 ◽

Vol 117 (47) ◽

pp. 29330-29337 ◽

Cited By ~ 1

Author(s):

Tal Golan ◽

Prashant C. Raju ◽

Nikolaus Kriegeskorte

Keyword(s):

Neural Networks ◽

Visual Recognition ◽

Deep Neural Networks ◽

Human Subjects ◽

Human Perception ◽

Ground Truth ◽

Natural Images ◽

Human Cognition ◽

Distribution Models ◽

Adversarial Examples

Distinct scientific theories can make similar predictions. To adjudicate between theories, we must design experiments for which the theories make distinct predictions. Here we consider the problem of comparing deep neural networks as models of human visual recognition. To efficiently compare models’ ability to predict human responses, we synthesize controversial stimuli: images for which different models produce distinct responses. We applied this approach to two visual recognition tasks, handwritten digits (MNIST) and objects in small natural images (CIFAR-10). For each task, we synthesized controversial stimuli to maximize the disagreement among models which employed different architectures and recognition algorithms. Human subjects viewed hundreds of these stimuli, as well as natural examples, and judged the probability of presence of each digit/object category in each image. We quantified how accurately each model predicted the human judgments. The best-performing models were a generative analysis-by-synthesis model (based on variational autoencoders) for MNIST and a hybrid discriminative–generative joint energy model for CIFAR-10. These deep neural networks (DNNs), which model the distribution of images, performed better than purely discriminative DNNs, which learn only to map images to labels. None of the candidate models fully explained the human responses. Controversial stimuli generalize the concept of adversarial examples, obviating the need to assume a ground-truth model. Unlike natural images, controversial stimuli are not constrained to the stimulus distribution models are trained on, thus providing severe out-of-distribution tests that reveal the models’ inductive biases. Controversial stimuli therefore provide powerful probes of discrepancies between models and human perception.

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Deep Neural Networks as a Computational Model for Human Shape Sensitivity

Journal of Vision ◽

10.1167/16.12.759 ◽

2016 ◽

Vol 16 (12) ◽

pp. 759

Author(s):

Jonas Kubilius ◽

Stefania Bracci ◽

Hans Op de Beeck

Keyword(s):

Neural Networks ◽

Computational Model ◽

Deep Neural Networks ◽

Shape Sensitivity

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Deep Neural Networks as a computational model for early vision: Lateral masking and contour integration

Journal of Vision ◽

10.1167/jov.20.11.1356 ◽

2020 ◽

Vol 20 (11) ◽

pp. 1356

Author(s):

Yoram Bonneh ◽

Ron Dekel

Keyword(s):

Neural Networks ◽

Computational Model ◽

Deep Neural Networks ◽

Contour Integration ◽

Early Vision ◽

Lateral Masking

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Deep Neural Networks as a Computational Model for Human Shape Sensitivity

PLoS Computational Biology ◽

10.1371/journal.pcbi.1004896 ◽

2016 ◽

Vol 12 (4) ◽

pp. e1004896 ◽

Cited By ~ 107

Author(s):

Jonas Kubilius ◽

Stefania Bracci ◽

Hans P. Op de Beeck

Keyword(s):

Neural Networks ◽

Computational Model ◽

Deep Neural Networks ◽

Shape Sensitivity

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MedicalGuard: U-Net Model Robust against Adversarially Perturbed Images

Security and Communication Networks ◽

10.1155/2021/5595026 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Hyun Kwon

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Human Perception ◽

Original Data ◽

Classification Model ◽

Medical Field ◽

Adversarial Examples ◽

Adversarial Training ◽

Fast Gradient ◽

Sign Method

Deep neural networks perform well for image recognition, speech recognition, and pattern analysis. This type of neural network has also been used in the medical field, where it has displayed good performance in predicting or classifying patient diagnoses. An example is the U-Net model, which has demonstrated good performance in data segmentation, an important technology in the field of medical imaging. However, deep neural networks are vulnerable to adversarial examples. Adversarial examples are samples created by adding a small amount of noise to an original data sample in such a way that to human perception they appear to be normal data but they will be incorrectly classified by the classification model. Adversarial examples pose a significant threat in the medical field, as they can cause models to misidentify or misclassify patient diagnoses. In this paper, I propose an advanced adversarial training method to defend against such adversarial examples. An advantage of the proposed method is that it creates a wide variety of adversarial examples for use in training, which are generated by the fast gradient sign method (FGSM) for a range of epsilon values. A U-Net model trained on these diverse adversarial examples will be more robust to unknown adversarial examples. Experiments were conducted using the ISBI 2012 dataset, with TensorFlow as the machine learning library. According to the experimental results, the proposed method builds a model that demonstrates segmentation robustness against adversarial examples by reducing the pixel error between the original labels and the adversarial examples to an average of 1.45.

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