Proposes a source-data-free transfer learning framework for sparse single-index models that transfers generalized Stein's lemma summaries and uses a guided MLP for nonlinear adaptation.
arXiv preprint arXiv:2003.12724 , year=
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CmIR uses causal inference to separate invariant causal representations from spurious ones in multimodal data, improving generalization under distribution shifts and noise via invariance, mutual information, and reconstruction constraints.
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Learning Invariant Modality Representation for Robust Multimodal Learning from a Causal Inference Perspective
CmIR uses causal inference to separate invariant causal representations from spurious ones in multimodal data, improving generalization under distribution shifts and noise via invariance, mutual information, and reconstruction constraints.