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Nov 13, 2019 · Abstract: We present Momentum Contrast (MoCo) for unsupervised visual representation learning. From a perspective on contrastive learning as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder.

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This is a PyTorch implementation of MoCo v3 for self-supervised ResNet and ViT. The original MoCo v3 was implemented in Tensorflow and run in TPUs. This repo re-implements in PyTorch and GPUs.

This is a PyTorch implementation of the MoCo paper: @Article{he2019moco, author = {Kaiming He and Haoqi Fan and Yuxin Wu and Saining Xie and Ross Girshick}, title = {Momentum Contrast for Unsupervised Visual Representation Learning}, journal = …

Mar 9, 2020 · Abstract: Contrastive unsupervised learning has recently shown encouraging progress, e.g., in Momentum Contrast (MoCo) and SimCLR. In this note, we verify the effectiveness of two of SimCLR's design improvements …

Feb 5, 2022 · Momentum Update for the Key Encoder, Outperforms Exemplar-CNN, Context Prediction, Jigsaw Puzzles, RotNet/Image Rotations, Colorization, DeepCluster, Instance Discrimination, CPCv1, CPCv2, CMC. A...

MoCo v2 is an improved version of the Momentum Contrast self-supervised learning algorithm. Motivated by the findings presented in the SimCLR paper, authors: Replace the 1-layer fully connected layer with a 2-layer MLP head with ReLU for the unsupervised training stage.

For the Earliest and Most Accurate Detection of Mild Cognitive Impairment. MoCA—also known as Montreal Cognitive Assessment or The MoCA Test—was validated as a highly sensitive tool for early detection of mild cognitive impairment (MCI) in 2000.