Representation Learning in Continuous-Time Score-Based Generative Models
Year: 2021
Type: preprint
Abstract: Score-based methods represented as stochastic differential equations on a continuous time domain have recently proven successful as a non-adversarial generative model. Training such models relies on denoising score matching, which can be seen as multi-scale denoising autoencoders. Here, we augment the denoising score-matching framework to enable representation learning without any supervised signal. GANs and VAEs learn representations by directly transforming latent codes to data samples. In contrast, score-based representation learning relies on a new formulation of the denoising score-matching objective and thus encodes information needed for denoising. We show how this difference allows for manual control of the level of detail encoded in the representation.
Source: arXiv (Cornell University)
Institutions: ETH Zurich, KTH Royal Institute of Technology
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3169303030