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Found 8 papers, 6 papers with code
Multi-Trigger Backdoor Attacks: More Triggers, More Threats
Arguably, real-world backdoor attacks can be much more complex, e. g., the existence of multiple adversaries for the same dataset if it is of high value.
LDReg: Local Dimensionality Regularized Self-Supervised Learning
Representations learned via self-supervised learning (SSL) can be susceptible to dimensional collapse, where the learned representation subspace is of extremely low dimensionality and thus fails to represent the full data distribution and modalities.
Distilling Cognitive Backdoor Patterns within an Image
We conduct extensive experiments to show that CD can robustly detect a wide range of advanced backdoor attacks.
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks
Specifically, we make the following key observations: 1) more parameters (higher model capacity) does not necessarily help adversarial robustness; 2) reducing capacity at the last stage (the last group of blocks) of the network can actually improve adversarial robustness; and 3) under the same parameter budget, there exists an optimal architectural configuration for adversarial robustness.
Unlearnable Examples: Making Personal Data Unexploitable
This paper raises the question: \emph{can data be made unlearnable for deep learning models?}
Neural Architecture Search via Combinatorial Multi-Armed Bandit
NAS can be performed via policy gradient, evolutionary algorithms, differentiable architecture search or tree-search methods.
Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial Robustness
Evaluating the robustness of a defense model is a challenging task in adversarial robustness research.
Normalized Loss Functions for Deep Learning with Noisy Labels
However, in practice, simply being robust is not sufficient for a loss function to train accurate DNNs.
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Image Classification
on mini WebVision 1.0
(ImageNet Top-1 Accuracy metric)
