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Exploring the Interplay of Interpretability and Robustness in Deep Neural Networks: A Saliency-guided Approach
Our strategy is grounded in the assumption that preserving salient features crucial for correctly classifying adversarial examples enhances model robustness, while masking non-relevant features improves interpretability.
Examining Changes in Internal Representations of Continual Learning Models Through Tensor Decomposition
We believe a scaled down version of our approach will provide insight into the benefits and pitfalls of using TCA to study continual learning dynamics.
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