如何看待谷歌发布迄今最大的视觉模型V-MoE，参数达150亿，在ImageNet上90.35%？
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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.
https://arxiv.org/abs/2106.05974
我是做nlp，有没有小伙伴科普一下，像那些经典Resnet，ResXnet模型等等的参数都有多大呀
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lz可以多介绍一些吗～
