Masked Generative

Masked generative modeling is a rapidly advancing area of machine learning focused on generating various data types (images, videos, audio, text, 3D models) by predicting masked portions of input sequences. Current research emphasizes transformer-based architectures, often incorporating techniques like vector quantization and hierarchical tokenization to improve efficiency and fidelity. This approach offers significant advantages in speed and scalability compared to traditional autoregressive methods, leading to improved performance in diverse applications such as text-to-image synthesis, video generation, and anomaly detection in time series data.