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Group-disentangled Representation Learning with Weakly-Supervised Regularization
Learning interpretable and human-controllable representations that uncover factors of variation in data remains an ongoing key challenge in representation learning.
Counterbalancing Teacher: Regularizing Batch Normalized Models for Robustness
Batch normalization (BN) is a ubiquitous technique for training deep neural networks that accelerates their convergence to reach higher accuracy.
Learning Task-Relevant Features via Contrastive Input Morphing
A fundamental challenge in artificial intelligence is learning useful representations of data that yield good performance on a downstream classification task, without overfitting to spurious input features.
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