Biologically inspired sleep algorithm for increased generalization and adversarial robustness in deep neural networks
Current artificial neural networks (ANNs) can perform and excel at a variety of tasks ranging from image classification to spam detection through training on large datasets of labeled data. While the trained network usually performs well on similar testing data, certain inputs that differ even slightly from the training data may trigger unpredictable behavior. Due to this limitation, it is possible to generate inputs with very small designed perturbations that can result in misclassification. These adversarial attacks present a security risk to deployed ANNs and indicate a divergence between how ANNs and humans perform classification. Humans are robust at behaving in the presence of noise and are capable of correctly classifying objects that are occluded, blurred, or otherwise distorted. It has been hypothesized that sleep promotes generalization and improves robustness against noise in animals and humans. In this work, we utilize a biologically inspired sleep phase in ANNs and demonstrate the benefit of sleep on defending against adversarial attacks as well as increasing ANN classification robustness. We compare the sleep algorithm*s performance on various robustness tasks with two previously proposed adversarial defenses, defensive distillation and fine-tuning. We report an increase in robustness after sleep to adversarial attacks as well as to general image distortions for three datasets: MNIST, CUB200, and a toy dataset. Overall, these results demonstrate the potential for biologically inspired solutions to solve existing problems in ANNs and guide the development of more robust, human-like ANNs.
The paper proposes an ANN training method for improving adversarial robustness and generalization, inspired by biological sleep. I*m leaning towards accepting as it seems to be an original concept and has fairly extensive empirical results that are somewhat promising. The idea of a sleep phase as an alternative to explicit adversarial or generalization training is interesting. The results suggest that the approach works reasonably well in many cases. Suggestions for improvement / clarification: - The mapping from biological sleep to the actual algorithm + pseudocode used could benefit from more thorough explanation. It is not clear which choices are arbitrary vs well-principled. - Was the optimal sleep duration determined empirically for each experiment? - I agree with the authors* proposed future work of better understanding and standardizing this approach. - Consider combining this approach with the existing adversarial or generalizing approaches (instead of as an alternative). Do they complement each other?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Biologically inspired sleep algorithm for increased generalization and adversarial robustness in deep neural networks
Current artificial neural networks (ANNs) can perform and excel at a variety of tasks ranging from image classification to spam detection through training on large datasets of labeled data. While the trained network usually performs well on similar testing data, certain inputs that differ even slightly from the training data may trigger unpredictable behavior. Due to this limitation, it is possible to generate inputs with very small designed perturbations that can result in misclassification. These adversarial attacks present a security risk to deployed ANNs and indicate a divergence between how ANNs and humans perform classification. Humans are robust at behaving in the presence of noise and are capable of correctly classifying objects that are occluded, blurred, or otherwise distorted. It has been hypothesized that sleep promotes generalization and improves robustness against noise in animals and humans. In this work, we utilize a biologically inspired sleep phase in ANNs and demonstrate the benefit of sleep on defending against adversarial attacks as well as increasing ANN classification robustness. We compare the sleep algorithm*s performance on various robustness tasks with two previously proposed adversarial defenses, defensive distillation and fine-tuning. We report an increase in robustness after sleep to adversarial attacks as well as to general image distortions for three datasets: MNIST, CUB200, and a toy dataset. Overall, these results demonstrate the potential for biologically inspired solutions to solve existing problems in ANNs and guide the development of more robust, human-like ANNs.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



The paper proposes an ANN training method for improving adversarial robustness and generalization, inspired by biological sleep. I*m leaning towards accepting as it seems to be an original concept and has fairly extensive empirical results that are somewhat promising. The idea of a sleep phase as an alternative to explicit adversarial or generalization training is interesting. The results suggest that the approach works reasonably well in many cases. Suggestions for improvement / clarification: - The mapping from biological sleep to the actual algorithm + pseudocode used could benefit from more thorough explanation. It is not clear which choices are arbitrary vs well-principled. - Was the optimal sleep duration determined empirically for each experiment? - I agree with the authors* proposed future work of better understanding and standardizing this approach. - Consider combining this approach with the existing adversarial or generalizing approaches (instead of as an alternative). Do they complement each other?
assistant
The paper proposes an ANN training method for improving adversarial robustness and generalization, inspired by biological sleep. I*m leaning towards accepting as it seems to be an original concept and has fairly extensive empirical results that are somewhat promising. The idea of a sleep phase as an alternative to explicit adversarial or generalization training is interesting. The results suggest that the approach works reasonably well in many cases. Suggestions for improvement / clarification: - The mapping from biological sleep to the actual algorithm + pseudocode used could benefit from more thorough explanation. It is not clear which choices are arbitrary vs well-principled. - Was the optimal sleep duration determined empirically for each experiment? - I agree with the authors* proposed future work of better understanding and standardizing this approach. - Consider combining this approach with the existing adversarial or generalizing approaches (instead of as an alternative). Do they complement each other?