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Found 2 papers, 1 papers with code
Rethinking Empirical Evaluation of Adversarial Robustness Using First-Order Attack Methods
We identify three common cases that lead to overestimation of adversarial accuracy against bounded first-order attack methods, which is popularly used as a proxy for adversarial robustness in empirical studies.
Autoencoder-Based Incremental Class Learning without Retraining on Old Data
Incremental class learning, a scenario in continual learning context where classes and their training data are sequentially and disjointedly observed, challenges a problem widely known as catastrophic forgetting.
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