Spatiotemporal Sequence Memory for Prediction using Deep Sparse Coding

Kim, Edward; Lawson, E.; Sullivan, Keith; Kenyon, Garrett T.

doi:10.1145/3320288.3320295

Cited by 3 publications

(1 citation statement)

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“…The final component of our framework is the model of the primary visual cortex as illustrated in Figure 3(c). The algorithm governing our approach to create a plausible neural representation is based upon deep sparse coding [26,27]. Mathematically, sparse coding is a reconstruction minimization problem which can be defined as follows.…”

Section: The Model Of Primary Visual Cortexmentioning

confidence: 99%

Modeling Biological Immunity to Adversarial Examples

Kim

Rego

Watkins

et al. 2020

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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While deep learning continues to permeate through all fields of signal processing and machine learning, a critical exploit in these frameworks exists and remains unsolved. These exploits, or adversarial examples, are a type of signal attack that can change the output class of a classifier by perturbing the stimulus signal by an imperceptible amount. The attack takes advantage of statistical irregularities within the training data, where the added perturbations can "move" the image across deep learning decision boundaries. What is even more alarming is the transferability of these attacks to different deep learning models and architectures. This means a successful attack on one model has adversarial effects on other, unrelated models. In a general sense, adversarial attack through perturbations is not a machine learning vulnerability. Human and biological vision can also be fooled by various methods, i.e. mixing high and low frequency images together, by altering semantically related signals, or by sufficiently distorting the input signal. However, the amount and magnitude of such a distortion required to alter biological perception is at a much larger scale. In this work, we explored this gap through the lens of biology and neuroscience in order to understand the robustness exhibited in human perception. Our experiments show that by leveraging sparsity and modeling the biological mechanisms at a cellular level, we are able to mitigate the effect of adversarial alterations to the signal that have no perceptible meaning. Furthermore, we present and illustrate the effects of top-down functional processes that contribute to the inherent immunity in human perception in the context of exploiting these properties to make a more robust machine vision system.

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Section: The Model Of Primary Visual Cortexmentioning

confidence: 99%