MoCo, or Momentum Contrast, is a self-supervised learning algorithm with a contrastive loss.
Contrastive loss methods can be thought of as building dynamic dictionaries. The "keys" (tokens) in the dictionary are sampled from data (e.g., images or patches) and are represented by an encoder network. Unsupervised learning trains encoders to perform dictionary look-up: an encoded “query” should be similar to its matching key and dissimilar to others. Learning is formulated as minimizing a contrastive loss.
MoCo can be viewed as a way to build large and consistent dictionaries for unsupervised learning with a contrastive loss. In MoCo, we maintain the dictionary as a queue of data samples: the encoded representations of the current mini-batch are enqueued, and the oldest are dequeued. The queue decouples the dictionary size from the mini-batch size, allowing it to be large. Moreover, as the dictionary keys come from the preceding several mini-batches, a slowly progressing key encoder, implemented as a momentum-based moving average of the query encoder, is proposed to maintain consistency.Source:
Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset Biases
Parametric Instance Classification for Unsupervised Visual Feature Learning
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What makes instance discrimination good for transfer learning?
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Improved Baselines with Momentum Contrastive Learning
Learning Speaker Embedding with Momentum Contrast
Momentum Contrast for Unsupervised Visual Representation Learning
|Self-Supervised Image Classification||2||15.38%|
|Unsupervised Representation Learning||1||7.69%|