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- W4307912078 abstract "Deep neural networks suffer from poor generalization to unseen environments when the underlying data distribution is different from that in the training set. By learning minimum sufficient representations from training data, the information bottleneck (IB) approach has demonstrated its effectiveness to improve generalization in different AI applications. In this work, we propose a new neural network-based IB approach, termed gated information bottleneck (GIB), that dynamically drops spurious correlations and progressively selects the most task-relevant features across different environments by a trainable soft mask (on raw features). Using the recently proposed matrix-based Rényi’s $$alpha $$ -order mutual information estimator, GIB enjoys a simple and tractable objective, without any variational approximation or distributional assumption. We empirically demonstrate the superiority of GIB over other popular neural network-based IB approaches in adversarial robustness and out-of-distribution detection. Meanwhile, we also establish the connection between IB theory and invariant causal representation learning and observed that GIB demonstrates appealing performance when different environments arrive sequentially, a more practical scenario where invariant risk minimization fails." @default.
- W4307912078 created "2022-11-06" @default.
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- W4307912078 date "2022-10-31" @default.
- W4307912078 modified "2023-09-28" @default.
- W4307912078 title "Gated information bottleneck for generalization in sequential environments" @default.
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- W4307912078 doi "https://doi.org/10.1007/s10115-022-01770-w" @default.
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