Review — MoCo v2: Improved Baselines with Momentum Contrastive Learning

MoCo v2, Improves MoCo Using SimCLR Suggestions

A batching perspective of two optimization mechanisms for contrastive learning (Left: SimCLR, Right: MoCo)

Improved Baselines with Momentum Contrastive Learning
MoCo v2
, by Facebook AI Research (FAIR)
2020 arXiv, Over 700 Citations (Sik-Ho Tsang @ Medium)
Self-Supervised Learning, Unsupervised Learning, Contrastive Learning, Representation Learning, Image Classification, Object Detection

  • SimCLR systematically studies the major components for the success of self-supervised contrastive learning.
  • MoCo v2 is achieved by enhancing MoCo v1 based on SimCLR’s studies: using an MLP projection head and more data augmentation.
  • Indeed, this tech report only got 2 pages excluding the reference section.


  1. MoCo v2
  2. Experimental Results

1. MoCo v2

1.1. Background

  • Contrastive loss function, called InfoNCE, in CPC, is used:
  • where q is a query representation, k+ is a representation of the positive (similar) key sample, and {k-} are representations of the negative (dissimilar) key samples. τ is a temperature hyperparameter.
  • End-to-End (Fig. a): In an end-to-end mechanism, the negative keys are from the same batch and updated end-to-end by back-propagation. SimCLR is based on this mechanism and requires a large batch to provide a large set of negatives.
  • Memory Bank (Fig. b): The negative keys are maintained in a queue, and only the queries and positive keys are encoded in each training batch.
  • In MoCo, a momentum encoder is adopted to improve the representation consistency between the current and earlier keys. MoCo decouples the batch size from the number of negatives.

1.2. Improved Designs

  • SimCLR improves the end-to-end variant of instance discrimination in three aspects: (i) a substantially larger batch (4k or 8k) that can provide more negative samples; (ii) replacing the output fc projection head [16] with an MLP head; (iii) stronger data augmentation.
  • In the MoCo framework, a large number of negative samples are readily available.

To enhance MoCo, the MLP head and data augmentation are used.

2. Experimental Results

2.1. Temperature

Search for an optimal τ w.r.t. ImageNet linear classification accuracy
  • Using the default τ=0.07, pre-training with the MLP head improves from 60.6% to 62.9%; switching to the optimal value for MLP (0.2), the accuracy increases to 66.2%.

2.2. MLP Head, Augmentation, Cosine Learning

Ablation of MoCo baselines, evaluated by ResNet-50 for (i) ImageNet linear classification, and (ii) fine-tuning VOC object detection (mean of 5 trials).
  • MLP: replace the fc head in MoCo with a 2-layer MLP head (hidden layer 2048-d, with ReLU).
  • aug+: Extra blur augmentation is added onto the default one.
  • cos: cosine learning rate schedule.

MLP+aug+cos improves MoCo v1. With longer training time of 800 epoches, results are even better.

2.3. SOTA Comparison

MoCo vs. SimCLR: ImageNet linear classifier accuracy (ResNet-50, 1-crop 224×224)

With MLP, aug+, cos, and longer training time (800 epochs), MoCo v2 achieves 71.1%, outperforming SimCLR’s 69.3% with 1000 epochs.

2.4. Computational Cost

Memory and time cost in 8 V100 16G GPUs
  • For end-to-end mechanism, the 4k batch size is intractable even in a high-end 8-GPU machine. Also, under the same batch size of 256, the end-to-end variant is still more costly in memory and time, because it back-propagates to both q and k encoders, while MoCo back-propagates to the q encoder only.

Later on, MoCo extends as MoCo v3 by using Vision Transformer, ViT.



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