In this paper, we propose a Collaboration of Experts (CoE) framework to pool together the expertise of multiple networks towards a common aim. Each expert is an individual network with expertise on a unique portion of the dataset, which enhances the collective capacity. Given a sample, an expert is selected by the delegator, which simultaneously outputs a rough prediction to support early termination. To fulfill this framework, we propose three modules to impel each model to play its role, namely weight generation module (WGM), label generation module (LGM) and variance calculation module (VCM). Our method achieves the state-of-the-art performance on ImageNet, 80.7% top-1 accuracy with 194M FLOPs. Combined with PWLU activation function and CondConv, CoE further achieves the accuracy of 80.0% with only 100M FLOPs for the first time. More importantly, our method is hardware friendly and achieves a 3-6x speedup compared with some existing conditional computation approaches.

本论文提出了一种Collaboration of Experts (CoE)框架，它可以将多个网络的专业知识汇集起来，以达到更好的性能表现。该框架由权重生成模块、标签生成模块和方差计算模块三个模块组成，可以实现对于ImageNet数据集的最优性能，并比某些现有的条件计算方法提供了3-6倍的加速。

专家合作：100M FLOPs在ImageNet上实现80%的Top-1准确率