1、目的

        无监督表示学习在自然图像领域已经很成功，因为语言任务有离散的信号空间（words, sub-word units等），便于构建tokenized字典

        现有的无监督视觉表示学习方法可以看作是构建动态字典，字典的“keys”则是从数据（images or patches）中采样得到的，并用编码网络来代表

        构建的字典需要满足large和consistent as they evolve during training这两个条件

2、方法

        Momentum Contrast (MoCo)

  

        1）contrastive learning

                dictionary look-up

                -> loss: info NCE

                        

                -> momentum

                        the dictionary is dynamic: the keys are randomly sampled, and the key encoder evolves during training

        2）dictionary as a queue

                -> large: decouple the dictionary size (can be set as a hyper-parameter) from the mini-batch size

                -> consistent: the encoded representations of the current mini-batch are enqueued, and the oldest are dequeued.

                                        the dictionary keys come from the preceding several mini-batches, slowly progressing key encoder, momentum-based moving average of the query encoder

        3）momentum update

                

                -> 只有的参数是通过back-propagation更新的

                -> 尽管不同mini-batch中的key是用不同的encoder编码的，这些encoder之间的差异比较小

        4）pretext task

                instance discrimination: a query matches a key if they are encoded views (e.g. different crops) of the same image

        5）shuffling BN

                perform BN on the samples independently for each GPU，以防intra-batch communication among samples造成信息泄露