Discrete Latent Representation

Discrete latent representation focuses on encoding data into discrete, rather than continuous, latent variables, aiming for improved interpretability, disentanglement of factors, and efficient data compression. Current research emphasizes vector quantization (VQ)-based autoencoders and variational autoencoders (VAEs), often incorporating techniques like residual vector quantization and autoregressive modeling to enhance the quality and diversity of generated data. These methods find applications across diverse fields, including speech processing, time series analysis, image generation, and human pose estimation, offering improvements in data generation, representation learning, and downstream task performance.