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Found 4 papers, 2 papers with code
Dataset Condensation with Contrastive Signals
However, in this study, we prove that the existing DC methods can perform worse than the random selection method when task-irrelevant information forms a significant part of the training dataset.
Learning Fair Classifiers with Partially Annotated Group Labels
To address this problem, we propose a simple Confidence-based Group Label assignment (CGL) strategy that is readily applicable to any fairness-aware learning method.
Fair Feature Distillation for Visual Recognition
Fairness is becoming an increasingly crucial issue for computer vision, especially in the human-related decision systems.
Continual Learning with Node-Importance based Adaptive Group Sparse Regularization
We propose a novel regularization-based continual learning method, dubbed as Adaptive Group Sparsity based Continual Learning (AGS-CL), using two group sparsity-based penalties.
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