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Found 3 papers, 1 papers with code
Toward Understanding Supervised Representation Learning with RKHS and GAN
At the population level, we formulate the ideal representation learning task as that of finding a nonlinear map that minimizes the sum of losses characterizing conditional independence (with RKHS) and disentanglement (with GAN).
Ranked #4 on
Image Classification
on Kuzushiji-MNIST
Sufficient and Disentangled Representation Learning
We provide strong statistical guarantees for the learned representation by establishing an upper bound on the excess error of the objective function and show that it reaches the nonparametric minimax rate under mild conditions.
Deep Dimension Reduction for Supervised Representation Learning
We propose a deep dimension reduction approach to learning representations with these characteristics.
