The search for "paper: 405rar" refers to , a recent paper published in November 2024 that introduces a new state-of-the-art model for image generation. Overview of RAR
: The paper and its associated codebase are available through platforms like arXiv and GitHub . Related Benchmarks & Agents 405rar
RAR is an autoregressive (AR) image generator designed to be fully compatible with standard language modeling frameworks. It aims to bridge the gap between traditional AR models and more flexible bidirectional models like diffusion or masked transformers. The search for "paper: 405rar" refers to ,
: On the ImageNet-256 benchmark, RAR achieved a FID score of 1.48 , which is a significant improvement over previous autoregressive generators and even outperforms many top-tier diffusion-based and masked transformer models. It aims to bridge the gap between traditional
It is important to distinguish the image generation model from other similarly named research: