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868_1_rp.rar -

Published in , this paper introduces a new state-of-the-art method for generating images using an autoregressive (AR) framework.

If you have downloaded this specific file and need to access its contents (which typically include code, models, or datasets), you will need specialized software:

: The model starts with high randomness (permuted order) and gradually returns to the standard raster order as training progresses. 868_1_RP.rar

: 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.

: RAR maintains full compatibility with standard language modeling frameworks, making it easier to integrate with existing AI architectures. Managing the .rar File Published in , this paper introduces a new

: 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.

Paper Overview: Randomized Autoregressive Visual Generation (RAR) RAR introduces Randomized Autoregressive modeling

: Use utilities like WinRAR or 7-Zip to unpack the archive.

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