Discrete Representation

Discrete representation learning focuses on encoding continuous data into discrete, often symbolic, formats to improve efficiency, interpretability, and generalization in machine learning models. Current research emphasizes the use of variational autoencoders (VAEs), particularly vector quantized VAEs (VQ-VAEs), and transformers to learn these representations, often incorporating techniques like quantization and codebook optimization to enhance disentanglement and performance. This approach is proving valuable across diverse applications, including speech synthesis, image generation, 3D mapping, and reinforcement learning, by enabling more efficient model training, improved data compression, and the development of more interpretable models.