Exploring Scaling in Auto-Encoders: Meta AI and UT Austin Introduce ViTok, a ViT-Style Auto-Encoder for Enhanced Performance
ViTok, developed by researchers from Meta and UT Austin, innovates image and video tokenization by utilizing a Vision Transformer-based auto-encoder. This approach moves away from traditional convolutional neural networks, addressing challenges in scalability and dataset constraints. ViTok focuses on optimizing three key areas: the size of the latent space for better reconstruction, increasing encoder complexity, ...
