Sparsely-gated Mixture of Experts networks (MoEs) have demonstrated excellent
scalability in Natural Language Processing. In Computer Vision, however, almost
all performant networks are "dense", that is, every input is processed by every
parameter. We present a Vision MoE (V-MoE), a sparse version of the Vision
Transformer, that is scalable and competitive with the largest dense networks.
When applied to image recognition, V-MoE matches the performance of
state-of-the-art networks, while requiring as little as half of the compute at
inference time. Further, we propose an extension to the routing algorithm that
can prioritize subsets of each input across the entire batch, leading to
adaptive per-image compute. This allows V-MoE to trade-off performance and
compute smoothly at test-time. Finally, we demonstrate the potential of V-MoE
to scale vision models, and train a 15B parameter model that attains 90.35% on
ImageNet.

本文介绍了一种名为 Vision MoE 的稀疏版本的 Vision Transformer，在图像识别方面表现出色且需要的计算量较少。此外，本文还提出了一种路由算法的扩展来优化每个输入的处理，最终通过训练一个达到 153 亿参数的模型，V-MoE 成功实现了高度可扩展的视觉建模。