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06/10/2023
Boosting Adversarial Robustness using Feature Level Stochastic Smoothing
Advances in adversarial defenses have led to a significant improvement i...
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04/20/2023
Certified Adversarial Robustness Within Multiple Perturbation Bounds
Randomized smoothing (RS) is a well known certified defense against adve...
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02/28/2023
DART: Diversify-Aggregate-Repeat Training Improves Generalization of Neural Networks
Generalization of neural networks is crucial for deploying them safely i...
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10/27/2022
Efficient and Effective Augmentation Strategy for Adversarial Training
Adversarial training of Deep Neural Networks is known to be significantl...
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10/18/2022
Towards Efficient and Effective Self-Supervised Learning of Visual Representations
Self-supervision has emerged as a propitious method for visual represent...
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10/18/2022
Scaling Adversarial Training to Large Perturbation Bounds
The vulnerability of Deep Neural Networks to Adversarial Attacks has fue...
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10/04/2022
Learning an Invertible Output Mapping Can Mitigate Simplicity Bias in Neural Networks
Deep Neural Networks are known to be brittle to even minor distribution ...
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08/19/2022
DAFT: Distilling Adversarially Fine-tuned Models for Better OOD Generalization
We consider the problem of OOD generalization, where the goal is to trai...
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04/23/2022
Towards Data-Free Model Stealing in a Hard Label Setting
Machine learning models deployed as a service (MLaaS) are susceptible to...
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11/30/2020
Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses
Advances in the development of adversarial attacks have been fundamental...
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07/31/2020
Saliency-driven Class Impressions for Feature Visualization of Deep Neural Networks
In this paper, we propose a data-free method of extracting Impressions o...
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04/01/2020
Towards Achieving Adversarial Robustness by Enforcing Feature Consistency Across Bit Planes
As humans, we inherently perceive images based on their predominant feat...
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12/27/2019
