CLIP-Mamba: CLIP Pretrained Mamba Models with OOD and Hessian Evaluation
<|reference_start|>CLIP-Mamba: CLIP Pretrained Mamba Models with OOD and Hessian Evaluation: State space models and Mamba-based models have been increasingly applied across various domains, achieving state-of-the-art performance. This technical report introduces the first attempt to train a transferable Mamba model utilizing contrastive language-image pretraining (CLIP). We have trained Mamba models of varying sizes and undertaken comprehensive evaluations of these models on 26 zero-shot classification datasets and 16 out-of-distribution (OOD) datasets. Our findings reveal that a Mamba model with 67 million parameters is on par with a 307 million-parameter Vision Transformer (ViT) model in zero-shot classification tasks, highlighting the parameter efficiency of Mamba models. In tests of OOD generalization, Mamba-based models exhibit exceptional performance in conditions of OOD image contrast or when subjected to high-pass filtering. However, a Hessian analysis indicates that Mamba models feature a sharper and more non-convex landscape compared to ViT-based models, making them more challenging to train. The source code is available at https://github.com/raytrun/mamba-clip.<|reference_end|>
@article{huang2024clip-mamba:,
    title={CLIP-Mamba: CLIP Pretrained Mamba Models with OOD and Hessian Evaluation},
    author={Weiquan Huang and Yifei Shen and Yifan Yang},
    journal={arXiv preprint arXiv:2404.19394},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.19394},
    primaryClass={cs.CV}
}