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Recently in the field of unsupervised representation learning, strong identifiability results for disentanglement of causally-related latent variables have been established by exploiting certain side information, such as class labels, in addition to independence.
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Nonlinear ica using auxiliary variables and generalized contrastive learning
Aapo Hyvarinen, Hiroaki Sasaki, and Richard Turner · 2019
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A meta-transfer objective for learning to disentangle causal mechanisms
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Disentangling identifiable features from noisy data with structured nonlinear ica
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Lassonet: A neural network with feature sparsity
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