Vector Quantized Variational

Vector Quantized Variational Autoencoders (VQ-VAEs) are generative models that learn discrete representations of data, offering advantages in data compression, generation, and downstream tasks like clustering and anomaly detection. Current research focuses on improving VQ-VAE architectures, including addressing codebook collapse, enhancing semantic control, and adapting codebook size dynamically for improved efficiency and performance across diverse data modalities. These advancements are impacting various fields, from robotics and motion synthesis to medical imaging and satellite image analysis, by enabling more efficient data handling, improved generative capabilities, and more robust feature extraction for complex tasks.