In this paper, we propose a Collaboration of Experts (CoE) framework to assemble the expertise of multiple networks towards a common goal. Each expert is an individual network with expertise on a unique portion of the dataset, contributing to the collective capacity. Given a sample, delegator selects an expert and simultaneously outputs a rough prediction to trigger potential early termination. For each model in CoE, we propose a novel training algorithm with two major components: weight generation module (WGM) and label generation module (LGM). It fulfills the co-adaptation of experts and delegator. WGM partitions the training data into portions based on delegator via solving a balanced transportation problem, then impels each expert to focus on one portion by reweighting the losses. LGM generates the label to constitute the loss of delegator for expert selection. CoE achieves the state-of-the-art performance on ImageNet, 80.7% top-1 accuracy with 194M FLOPs. Combined with PWLU and CondConv, CoE further boosts the accuracy to 80.0% with only 100M FLOPs for the first time. Furthermore, experiment results on the translation task also demonstrate the strong generalizability of CoE. CoE is hardware-friendly, yielding a 3~6x acceleration compared with existing conditional computation approaches.