Quantization VAE

Quantization-based variational autoencoders (VAEs) aim to learn efficient, discrete representations of data by encoding continuous latent vectors into a finite codebook. Current research focuses on improving codebook learning through techniques like masked quantization, dynamic quantization, and simplified scalar quantization, addressing issues such as codebook collapse and redundancy. These advancements enhance the performance of downstream tasks, such as image generation and speech synthesis, by enabling more accurate and compact data representations. The resulting improvements in efficiency and quality have significant implications for various applications requiring high-dimensional data processing.