: RAR maintains full compatibility with standard language modeling frameworks, making it easier to integrate with existing AI architectures. Managing the .rar File
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Published in , this paper introduces a new state-of-the-art method for generating images using an autoregressive (AR) framework.
: It achieved a Frechet Inception Distance (FID) score of 1.48 on the ImageNet-256 benchmark, outperforming many leading diffusion-based and masked transformer models.
: Standard AR models generate images in a fixed "raster" order (like reading a book), which limits their ability to understand the whole image at once. RAR introduces Randomized Autoregressive modeling , which randomly permutes the order of image tokens during training.
: The model starts with high randomness (permuted order) and gradually returns to the standard raster order as training progresses.
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Paper Overview: Randomized Autoregressive Visual Generation (RAR)
