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## MoCo

| Title:       | Momentum Contrast for Unsupervised Visual Representation Learning (MoCo) |
| ------------ | ---- |
| **Authors:** | Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, Ross Girshick |
| **Blog:**    |      |
| **Read:**    | ✓   |
| **Referee:** |      |

### Motivation

The task of producing a feature extractor through Contrastive Learning as Dictionary Look-up involves training a query encoder and a key encoder, to make the encoding of the query and positive key similar, and the encodings of the query and negative keys dissimilar. It has been found that a large number of negative keys must be used for the method to be effective, which makes end-to-end training computationaly intractable.

### Approach

**MoCo is an algorithm to train the key encoder, which is more scalable than end-to-end training.** It suggests we only train the query encoder through back-propagation, and set the parameters of the key encoder to a weighted average of the parameters of the query encoder and the previous parameters of the key encoder. Prior to MoCo, the only mechanism available to accomplish this was [Memory Bank](https://arxiv.org/pdf/1805.01978v1.pdf).

### Experiments

#### Linear classification

The feature extractors through self-supervised learning using MoCo, Memory Bank and end-to-end training were benchmarked on ImageNet under the linear classification protocol. MoCo outperformed Memory Bank, and performed competitively with end-to-end training.

#### Transfering features for downstream tasks

The MoCo feature extractors trained on ImageNet and Instagram-1B were benchmarked against one that was obtained through supervised training on ImageNet, on the 6 downstream tasks.

On 5 tasks, the Instagram feature extractor outperforms the supervised counterpart, and the ImageNet feature extractor outperforms it or performs competitively with it. On 1 task, the supervised counterpart outperforms both feature extractors.