Autoregressive Pretraining with Mamba in Vision
Keywords: Auto regressive Pretraining
Abstract: The vision community has started to build with the recently developed state space model, Mamba, as the new backbone for a range of tasks. This paper shows that Mamba's visual capability can be significantly enhanced through autoregressive pretraining, a direction not previously explored. Efficiency-wise, the autoregressive nature can well capitalize on the Mamba's unidirectional recurrent structure, enabling faster overall training speed and reduced GPU memory usage compared to other training strategies. Performance-wise, autoregressive pretraining equips the Mamba architecture with markedly higher accuracy over its supervised-trained counterparts and, more importantly, successfully unlocks its scaling potential to large and even huge model sizes. For example, with autoregressive pretraining, a base-size Mamba outperforms its supervised counterpart by 2.0% on ImageNet classification; our best model, a huge-size Mamba, attains 85.0% top-1 ImageNet accuracy, significantly outperforming all existing Mamba variants in vision.
Primary Area: foundation or frontier models, including LLMs
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Submission Number: 7773
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