simclr
SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners (by google-research)
moco
PyTorch implementation of MoCo: https://arxiv.org/abs/1911.05722 (by facebookresearch)
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| simclr | moco | |
|---|---|---|
| 13 | 4 | |
| 3,927 | 4,550 | |
| 1.8% | 2.3% | |
| 2.9 | 3.7 | |
| 11 months ago | 4 months ago | |
| Jupyter Notebook | Python | |
| Apache License 2.0 | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
simclr
Posts with mentions or reviews of simclr.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-03-25.
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Positive and Negative Sampling Strategies for Representation Learning in Semantic Search
For visual representations, you could look into SimCLR and MoCo. https://github.com/google-research/simclr https://github.com/facebookresearch/moco
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[D] Why is random cropping necessary in SimCLR?
Yeah I think so, it's not hard to check https://github.com/google-research/simclr/blob/2fc637bdd6a723130db91b377ac15151e01e4fc2/data_util.py
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[R] Deep Learning with a Small Training Batch (or Lack Thereof). Part 1
Code for https://arxiv.org/abs/2006.10029 found: https://github.com/google-research/simclr
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[D] Current trends in computer vision related to unsupervised learning
SimCLR v2.0 - https://arxiv.org/abs/2006.10029
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Self-Supervised Contrastive Learning model for video dataset?
My data consists of binary labels (normal & anomalous) where the videos are already broken up into frames in the directory, I'm looking for a model where I can feed a normal-labeled video alongside an anomalous-labeled video like visualized in this example from the SimCLR Repo, a dog will represent the normal video and the chair the anomalous video.
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[P] Choosing a self-supervised learning framework that's easy to use
No, go to the "tf2" folder in the repo root. https://github.com/google-research/simclr/tree/master/tf2
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[D] What is meant by width in the SimCLRv2 paper?
Code for https://arxiv.org/abs/2006.10029 found: https://github.com/google-research/simclr
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[D] Funding PhD in Europe
[1] https://github.com/google-research/simclr [2] https://www.tensorflow.org/tfrc?hl=en&authuser=2
moco
Posts with mentions or reviews of moco.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-03-25.
-
Positive and Negative Sampling Strategies for Representation Learning in Semantic Search
For visual representations, you could look into SimCLR and MoCo. https://github.com/google-research/simclr https://github.com/facebookresearch/moco
- [D] Self-Supervised Contrastive Approaches that don’t use large batch size.
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[D] Significant differences in Training on 1 GPU vs 8GPU ?
I am looking at the official code for MOCO-v2 linear evaluation here: https://github.com/facebookresearch/moco The command to execute suggests running a 256 sized batch across 8 GPUs for a Resnet-50. Do keep in mind that for the linear evaluation the model is frozen till the end and only the final fully connected layer is fine-tuned.
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[D] Momentum Contrast for Unsupervised Visual Representation Learning MoCo v1 & v2 by Kwonjoon Lee et al.
[Full Explanation Post / Blog Post] [Arxiv] [Code]
What are some alternatives?
When comparing simclr and moco you can also consider the following projects:
swav - PyTorch implementation of SwAV https//arxiv.org/abs/2006.09882
Unsupervised-Classification - SCAN: Learning to Classify Images without Labels, incl. SimCLR. [ECCV 2020]
unsupervised-depth-completion-visual-inertial-odometry - Tensorflow and PyTorch implementation of Unsupervised Depth Completion from Visual Inertial Odometry (in RA-L January 2020 & ICRA 2020)
SimCLR - PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations
torchlars - A LARS implementation in PyTorch
Supervised-Constrastive-Learning-in-TensorFlow-2 - Implements the ideas presented in https://arxiv.org/pdf/2004.11362v1.pdf by Khosla et al. [Moved to: https://github.com/sayakpaul/Supervised-Contrastive-Learning-in-TensorFlow-2]
CodeSearchNet - Datasets, tools, and benchmarks for representation learning of code.
soft-vc - Soft speech units for voice conversion