87213955efbe48b46586e37bf2f1fe5b-Paper-Conference.pdf

Autoencoders (AEs) [1, 2] and its modern variants like the widely used variational autoencoders (VAEs) [3], are a powerful paradigm for self-supervised representation learning for generative modeling [4], compression [5], anomaly detection [6] or natural language processing [7]. Since autoencoders can learn low dimensional representations without requiring labeled data, they are particularly useful forcomputer vision taskswhere samples canbeveryhighdimensional making processing, transmitting, and search prohibitively expensive.