TiTok introduces a novel 1D tokenization method for image generation, enabling the representation of images with significantly fewer tokens while maintaining or surpassing the performance of existing 2D grid-based methods. The approach leverages a Vision Transformer architecture, two-stage training with proxy codes, and achieves remarkable speedup in training and inference. The research opens up new possibilities for efficient and high-quality image generation, with implications for various applications in computer vision and beyond.
Listen to the Episode
Related Links
The (AI) Team
- Alex Askwell: Our curious and knowledgeable moderator, always ready with the right questions to guide our exploration.
- Dr. Paige Turner: Our lead researcher and paper expert, diving deep into the methods and results.
- Prof. Wyd Spectrum: Our field expert, providing broader context and critical insights.
Listen on your favorite platforms
