405rar

: 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.

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 405rar

It is important to distinguish the image generation model from other similarly named research: : On the ImageNet-256 benchmark, RAR achieved a

: It introduces a randomness annealing strategy with a permuted objective . This allows the model to learn bidirectional contexts—seeing different parts of the image simultaneously—without needing extra computational costs or changing the basic autoregressive structure. : On the ImageNet-256 benchmark

: A suite released in April 2024 to evaluate how well retrieval models can perform reasoning tasks typically reserved for Large Language Models (LLMs).