kakaoenterprise / learning-debiased-disentangled Goto Github PK
View Code? Open in Web Editor NEWOfficial Pytorch implementation of "Learning Debiased Representation via Disentangled Feature Augmentation (Neurips 2021, Oral)"
Official Pytorch implementation of "Learning Debiased Representation via Disentangled Feature Augmentation (Neurips 2021, Oral)"
Hi,
Thanks for open-sourced the code! I made a local copy of the code base as well as the CMNIST dataset. Simply by running the model of MLP and MLP_disentangled with provided Linux command lines as instructed in README. The training loss of either case is not decreasing and hence the test accuracy is similar with a naive classifier (~10%). Could you look into this issue?
Thank you!
Hello, thank you very much for the open-source code. I tried to reconstruct CMNIST in the 1pt pre-training model you provided according to the information in the appendix but found that it seemed unable to reconstruct. Could you please provide the code of the reconstruction part?
At the dataset files description of cmnist and cifar10c
there's typo conlict -> conflict
Dear authors,
I want to know the data setting of validation set for CMNIST. Does the validation set have the same bias degree with training set? Thanks in advance.
Dear authors,
What's the official name for your method? I cannot find the name in your paper. I want to compare your method in my paper.
Thanks in advance.
Shaohua
Hello dear authors,
I was wondering if I could have some clarifications on GCE.
The reason I doubt my statement is due to your clarifications on GCE in OpenReview.
I thought GCE was more attentive to the bias element not because it emphasizes the easy samples but de-emphasizes the hard ones.
Thank you for your attention and great paper!
Hi,
Thanks for sharing the code, it's really helpful. From your code, I can get the acc of bias align, bias conflic and total acc. But could I know how can I calculate the unbiased accuracy? Thanks!
Hi,
Thanks for open-sourced the code! Can you please make the data generating code public? I hope to further modify the dataset to test the relationship between bias rate and performance. Your code seems clear from the released dataset, I would love to refer to it.
Thanks very much!
When I run the command
python train.py --dataset cmnist --exp=cmnist_0.5_ours --lr=0.01 --percent=0.5pct --curr_step=10000 --lambda_swap=1 --lambda_dis_align=10 --lambda_swap_align=10 --use_lr_decay --lr_decay_step=10000 --lr_gamma=0.5 --train_ours --tensorboard
the output is
...
valid_d: 0.11319999396800995 || test_d: 0.11349999904632568
97%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 48496/50000 [12:32<00:16, 88.77it/s
48500 model saved ...
valid_d: 0.11319999396800995 || test_d: 0.11349999904632568
98%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▋ | 48996/50000 [12:40<00:10, 96.10it/s]
49000 model saved ...
49000 model saved ...
valid_d: 0.11319999396800995 || test_d: 0.11349999904632568
99%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊ | 49500/50000 [12:49<00:07, 71.10it/s
49500 model saved ...
valid_d: 0.11319999396800995 || test_d: 0.11349999904632568
The test acc is 0.1135. However in the paper, the result is 65.22. I don't know what's wrong. Can anyone help me?
It appears that the result of the BAR dataset was in the paper during the submission phase(https://openreview.net/forum?id=-oUhJJILWHb) and was also appreciated by the reviewers. At the same time, it was removed from the final camera-ready version. Is there any particular reason for this? Could you provide the code for replicating the result on the BAR dataset?
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.