Reversible Vision Transformers

We present Reversible Vision Transformers, a memory efficient architecture design for visual recognition. By decoupling the GPU memory footprint from the depth of the model, Reversible Vision Transformers enable memory efficient scaling of transformer architectures. We adapt two popular models, namely Vision Transformer and Multiscale Vision Transformers, to reversible variants and benchmark extensively across both model sizes and tasks of image classification, object detection and video classification. Reversible Vision Transformers achieve a reduced memory footprint of up to 15.5× at identical model complexity, parameters and accuracy, demonstrating the promise of reversible vision transformers as an efficient backbone for resource limited training regimes. 2022: Karttikeya Mangalam, Haoqi Fan, Yanghao Li, Chaoxiong Wu, Bo Xiong, Christoph Feichtenhofer, J. Malik https://arxiv.org/pdf/2302.04869v1.pdf