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Found 3 papers, 2 papers with code
Wasserstein Geodesic Generator for Conditional Distributions
The conditional distributions given unobserved intermediate domains are on the Wasserstein geodesic between conditional distributions given two observed domain labels.
Covariate-informed Representation Learning to Prevent Posterior Collapse of iVAE
Though the identifiability is appealing, we show that iVAEs could have local minimum solution where observations and the approximated ICs are independent given covariates.-a phenomenon we referred to as the posterior collapse problem of iVAEs.
Kernel-convoluted Deep Neural Networks with Data Augmentation
The Mixup method (Zhang et al. 2018), which uses linearly interpolated data, has emerged as an effective data augmentation tool to improve generalization performance and the robustness to adversarial examples.
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