salesforce / comatch Goto Github PK
View Code? Open in Web Editor NEWCode for CoMatch: Semi-supervised Learning with Contrastive Graph Regularization
License: BSD 3-Clause "New" or "Revised" License
Code for CoMatch: Semi-supervised Learning with Contrastive Graph Regularization
License: BSD 3-Clause "New" or "Revised" License
Dear authors,
In Eq.10, you build the embedding graph with two strong augmentations, where the off-diagonal values are generated by enhancement 1 only. However, in your code, this is not implemented, this seems at odds with the paper description, could you explain it?
Thanks for sharing your clean codes.
While I find that the codes of distribution alignment (DA) only calculate:
q = Norm(q/mean(p(y))),
without multiplying labeled data distribution p(y) and further sharpening, which seems not same as the DA introduced in ReMixMatch. Wonder why don't use the standard DA to train models? Is the simple way you used better for SSL models' performance?
the idea in your paper is amazing,great truths are all simple. I have the following questions:
1、Does a stronger data enhancement method, such as RandomAugment, improve the performance?
2、To improve the performance, is necessary expend the size of distribution alignment ?
3、how to adjust the memory bank size when more label data is used in ImageNet ,such as 20% of ImageNet?
4、If use 20% of ImageNet data as a label, what are the recommendations for other hyperparameters?
5、From your point of view, what are the main challenges in achieving full oversight with 20% of ImageNet data?
Hello, I can't find the implementation of svhn dataset in this project, do you have open-source the code of this part? thank you!
Hi,
Thank you for your great work, I am new to this area, I have the following questions:
1、How much improvements can be made by the branch of Graph-based contrastive learning? I can not see that ablation experiments in the paper. I'm interested in this part and would also like to know the performance improvements that might be useful for my task.
2、Does the Co-match method fit for multi-label classification tasks?
Once again, thank you for your nice work and clean code! Looking forward to your reply.
Best regards!
Tan
Thanks a lot for sharing your clear codes. I try to train the models on STL dataset but don't find the dataloader of STL suitable for SSL setting. Would you like to share the codes of STL Dataloader with randomly splitting labeled&unlabeled data?
Hi! Have you conducted the qualitative ablation study of the designed components, including smoothed pseudo labels (equation 8), graph-based contrastive learning (equation11) and the memory bank? It seems that you only conduct the hyper-parameters ablation study. Thanks!
Hi, thanks for the nice code.
Could you please provide some information on where can I obtain the public mocoV2 pre-trained model.
Dear Author
Thank you for this exciting work!
I have a clarification question regarding the experiment setting: Did you use any validation data to tune the hyperparameters for CoMatch? How did u choose the alpha, lambda etc?
Dear Authors
Your work is exciting! I am trying out your code with the example you provides, python Train_CoMatch.py --n-labeled 40 --seed 1
I am running on one A100 gpu. I found that the speed is very slow, takes ~10 minutes for 1 epoch. Is this the expected behavior? I just want to make sure i am running the code correctly.
Thank you, and hope to hear back from you!
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.