The latent variables learned by VAEs have seen considerable interest as an unsupervised way of extracting features, which can then be used for downstream tasks. There is a growing interest in the question of whether features learned on one environment will generalize across different environments. We demonstrate here that VAE latent variables often focus on some factors of variation at the expense of others - in this case we refer to the features as ``imbalanced''. Feature imbalance leads to poor generalization when the latent variables are used in an environment where the presence of features changes. Similarly, latent variables trained with imbalanced features induce the VAE to generate less diverse (i.e. biased towards dominant features) samples. To address this, we propose a regularization scheme for VAEs, which we show substantially addresses the feature imbalance problem. We also introduce a simple metric to measure the balance of features in generated images.
Integrating Predictive and Generative Capabilities by Latent Space Design via the DKL-VAE Model
Center for Nanointegration Duisburg-Essen (CENIDE), University of Duisburg-Essen（杜伊斯堡-埃森大学纳米集成中心）；Department of Materials Science and Engineering, University of Tennessee（田纳西大学材料科学与工程系）；Pacific Northwest National Laboratory（太平洋西北国家实验室）
TaskVAE: Task-Specific Variational Autoencoders for Exemplar Generation in Continual Learning for Human Activity Recognition
Univ. Grenoble Alpes, CNRS, Grenoble INP（格拉诺布尔大学）；Institute of Engineering Univ. Grenoble Alpes（格拉诺布尔大学工程学院）；School of Computing and Engineering, Univ. of Huddersfield（赫德瑟尔菲尔德大学计算机与工程学院）
