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: 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 state-of-the-art method for generating images using an autoregressive (AR) framework. 868_1_RP.rar
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: : RAR maintains full compatibility with standard language
: 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 introduces Randomized Autoregressive modeling
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: The model starts with high randomness (permuted order) and gradually returns to the standard raster order as training progresses.