Title: A Bayesian Nonparametric Framework For Learning Disentangled Representations
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: representation learning, disentangled representations, unsupervised learning, nonparametric methods

Abstract: The field of unsupervised disentangled representation learning concerns itself with discovering and organizing patterns in a dataset without the use of an oracle capable of giving us the ground-truth labels. However, current approaches face fundamental challenges in determining both the appropriate latent space complexity needed to capture underlying generative factors and the optimal strength of regularization constraints required for effective disentangled representations. We address these challenges by proposing a Bayesian nonparametric prior over the embedding space of latent-quantization autoencoders, enabling the model to adaptively adjust representation capacity and regularization in accordance with the intrinsic data complexity. To support inference under this nonparametric prior, we introduce an infinite-capacity variational family which preserves the hierarchical prior structure and latent quantization biases while maintaining sufficient expressiveness to capture complex dependencies essential for effective representation learning. Our proposed approach consistently achieves superior or competitive performance relative to strong baseline methods on both 3DShapes and MPI3D datasets characterized by diverse source variation distributions. We empirically demonstrate that the proposed probabilistic framework achieves disentangled representation learning through intrinsic structural biases and a unified objective function, obviating the need for auxiliary regularization constraints or careful hyperparameter tuning.

TL;DR: A nonparametric variational inference framework for the unsupervised learning of disentangled representations