(L2ID@CVPR2021, TNNLS2022) Pytorch implementation of paper "Boosting Co-teaching with Compression Regularization for Label Noise" and "Compressing Features for Learning With Noisy Labels".
[PDF] [Video] [Journal PDF (TNNLS 2022)] [Project Page]
If our project is helpful for your research, please consider citing :
@inproceedings{chen2021boosting,
title={Boosting Co-teaching with Compression Regularization for Label Noise},
author={Chen, Yingyi and Shen, Xi and Hu, Shell Xu and Suykens, Johan AK},
booktitle={CVPR Learning from Limited and Imperfect Data (L2ID) workshop},
year={2021}
}
or
@ARTICLE{chen2022compressing,
title={Compressing Features for Learning with Noisy Labels},
author={Chen, Yingyi and Hu, Shell Xu and Shen, Xi and Ai, Chunrong and Suykens, Johan A. K.},
journal={IEEE Transactions on Neural Networks and Learning Systems},
year={2022}
}
Our model can be learnt in a single GPU GeForce GTX 1080Ti (12G), this code has been tested with Pytorch 1.7.1
The nested regularization allows us to learn ordered representation which would be useful to combat noisy label. In this toy example, we aim at learning a projection from X to Y with noisy pairs. By adding nested regularization, the most informative recontruction is stored in the first few channels.
| Baseline, same MLP | Nested200, 1st channel |
|---|---|
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| Nested200,first 10 channels | Nested200, first 100 channels |
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Clothing1M [Xiao et al., 2015]
- We provide average accuracy as well as the standard deviation for three runs (%) on the test set of Clothing1M [Xiao et al., 2015]. Results with “*“ are either using a balanced subset or a balanced loss.
| Methods | acc@1 | result_ref/download |
|---|---|---|
| CE | 67.2 | [Wei et al., 2020] |
| F-correction [Patrini et al., 2017] | 68.9 | [Wei et al., 2020] |
| Decoupling [Malach and Shalev-Shwartz, 2017] | 68.5 | [Wei et al., 2020] |
| Co-teaching [Han et al., 2018] | 69.2 | [Wei et al., 2020] |
| Co-teaching+ [Yu et al., 2019] | 59.3 | [Wei et al., 2020] |
| JoCoR [Wei et al., 2020] | 70.3 | -- |
| JO [Tanaka et al., 2018] | 72.2 | -- |
| Dropout* [Srivastava et al., 2014] | 72.8 | -- |
| PENCIL* [Yi and Wu, 2019] | 73.5 | -- |
| MLNT [Li et al., 2019] | 73.5 | -- |
| PLC* [Zhang et al., 2021] | 74.0 | -- |
| DivideMix* [Li et al., 2020] | 74.8 | -- |
| Nested* (Ours) | 73.1 ± 0.3 | model |
| Nested + Co-teaching* (Ours) | 74.9 ± 0.2 | model |
ANIMAL-10N [Song et al., 2019]
- We provide test set accuracy (%) on ANIMAL-10N [Song et al., 2019]. We report average accuracy as well as the standard deviation for three runs.
| Methods | acc@1 | result_ref/download |
|---|---|---|
| CE | 79.4 ± 0.1 | [Song et al., 2019] |
| Dropout [Srivastava et al., 2014] | 81.3 ± 0.3 | -- |
| SELFIE [Song et al., 2019] | 81.8 ± 0.1 | -- |
| PLC [Zhang et al., 2021] | 83.4 ± 0.4 | -- |
| Nested (Ours) | 81.3 ± 0.6 | model |
| Nested + Co-teaching (Ours) | 84.1 ± 0.1 | model |
To download Clothing1M dataset [Xiao et al., 2015], please refer to here. Once it is downloaded, put it into ./data/. The structure of the file should be:
./data/Clothing1M
├── noisy_train
├── clean_val
└── clean_test
Generate two random Clothing1M noisy subsets for training after unzipping :
cd data/
# generate two random subsets for training
python3 clothing1M_rand_subset.py --name noisy_rand_subtrain1 --data-dir ./Clothing1M/ --seed 123
python3 clothing1M_rand_subset.py --name noisy_rand_subtrain2 --data-dir ./Clothing1M/ --seed 321Please refer to data/gen_data.sh for more details.
To download ANIMAL-10N dataset [Song et al., 2019], please refer to here. It includes one training and one test set. Once it is downloaded, put it into ./data/. The structure of the file should be:
./data/Animal10N/
├── train
└── test
Download CIFAR-10/CIFAR-100 to ./data/. Once they are downloaded, please conduct the following preprocessing :
cd data/
## Split into train/val/test
python preprocess_cifar10.py
python preprocess_cifar100.pyPlease refer to ./data/create_cifar_noise.sh to generate the noisy traing sets. The structure of the file should be:
./data/CIFAR10 (./data/CIFAR100)
├── train
├── val
├── test
├── train_sn_0.2
├── train_sn_0.5
├── train_sn_0.8
├── train_an_0.3
└── train_an_0.5
We first train two Nested Dropout networks separately to provide reliable base networks for the subsequent stage. You can run the training of this stage by :
- For training networks on Clothing1M (ResNet-18). You can also train baseline/dropout networks for comparisons. More details are provided in nested/run_clothing1m.sh.
cd nested/
# train one Nested network
python3 train_resnet.py --train-dir ../data/Clothing1M/noisy_rand_subtrain1/ --val-dir ../data/Clothing1M/clean_val/ --dataset Clothing1M --arch resnet18 --lrSchedule 5 --lr 0.02 --nbEpoch 30 --batchsize 448 --nested 100 --pretrained --freeze-bn --out-dir ./checkpoints/Cloth1M_nested100_lr2e-2_bs448_freezeBN_imgnet_model1 --gpu 0- For training networks on ANIMAL-10N (VGG-19+BN). You can also train baseline/dropout networks for comparisons. More details are provided in nested/run_animal10n.sh.
cd nested/
python3 train_vgg.py --train-dir ../data/Animal10N/train/ --val-dir ../data/Animal10N/test/ --dataset Animal10N --arch vgg19-bn --lr-gamma 0.2 --batchsize 128 --warmUpIter 6000 --nested1 100 --nested2 100 --alter-train --out-dir ./checkpoints_animal10n/Animal10N_alter_nested100_100_vgg19bn_lr0.1_warm6000_bs128_model1 --gpu 0- For training networks on CIFAR-10/CIFAR-100 (ResNet-18). You can also train baseline/dropout networks for comparisons. More details are provided in nested/run_cifar.sh.
cd nested/
# CIFAR-10 Symmetric 20%, Nested=10
python train_resnet.py --train-dir ../data/CIFAR10/train_sn_0.2/ --val-dir ../data/CIFAR10/val/ --dataset CIFAR10 --arch resnet18 --out-dir ./checkpoints/cifar10sn0.2_nested10_model1 --nested 10 --gpu 0
# CIFAR-100 Symmetric 20%, Nested=100
python train_resnet.py --train-dir ../data/CIFAR100/train_sn_0.2/ --val-dir ../data/CIFAR100/val/ --dataset CIFAR100 --arch resnet18 --out-dir ./checkpoints/cifar100sn0.2_nested100_model1 --nested 100 --gpu 0In this stage, the two trained networks are further fine-tuned with Co-teaching. You can run the training of this stage by :
- For fine-tuning with Co-teaching on Clothing1M (ResNet-18) :
cd co_teaching_resnet/
python3 main.py --train-dir ../data/Clothing1M/noisy_rand_subtrain1/ --val-dir ../data/Clothing1M/clean_val/ --dataset Clothing1M --lrSchedule 5 --nGradual 0 --lr 0.002 --nbEpoch 30 --warmUpIter 0 --batchsize 448 --freeze-bn --forgetRate 0.3 --out-dir ./finetune_ckpt/Cloth1M_nested100_lr2e-3_bs448_freezeBN_fgr0.3_pre_nested100_100 --resumePthList ../nested/checkpoints/Cloth1M_nested100_lr2e-2_bs448_imgnet_freezeBN_model1_Acc0.735_K12 ../nested/checkpoints/Cloth1M_nested100_lr2e-2_bs448_imgnet_freezeBN_model2_Acc0.733_K15 --nested 100 --gpu 0The two Nested ResNet-18 networks trained in stage one can be downloaded here: ckpt1, ckpt2. We also provide commands for training Co-teaching from scratch for comparisons in co_teaching_resnet/run_clothing1m.sh.
- For fine-tuning with Co-teaching on ANIMAL-10N (VGG-19+BN) :
cd co_teaching_vgg/
python3 main.py --train-dir ../data/Animal10N/train/ --val-dir ../data/Animal10N/test/ --dataset Animal10N --arch vgg19-bn --lrSchedule 5 --nGradual 0 --lr 0.004 --nbEpoch 30 --warmUpIter 0 --batchsize 128 --freeze-bn --forgetRate 0.2 --out-dir ./finetune_ckpt/Animal10N_alter_nested100_lr4e-3_bs128_freezeBN_fgr0.2_pre_nested100_100_nested100_100 --resumePthList ../nested/checkpoints_animal10n/new_code_nested/Animal10N_alter_nested100_100_vgg19bn_lr0.1_warm6000_bs128_model1_Acc0.803_K14 ../nested/checkpoints_animal10n/new_code_nested/Animal10N_alter_nested100_100_vgg19bn_lr0.1_warm6000_bs128_model2_Acc0.811_K14 --nested1 100 --nested2 100 --alter-train --gpu 0The two Nested VGG-19+BN networks trained in stage one can be downloaded here: ckpt1, ckpt2. We also provide commands for training Co-teaching from scratch for comparisons in co_teaching_vgg/run_animal10n.sh.
- For fine-tuning with Co-teaching on CIFAR-10/CIFAR-100 (ResNet-18) :
cd co_teaching_resnet/
# CIFAR-10 Symmetric 20%, Nested=10
python3 main.py --train-dir ../data/CIFAR10/train_sn_0.2/ --val-dir ../data/CIFAR10/val/ --dataset CIFAR10 --lrSchedule 50 --nGradual 0 --lr 0.001 --nbEpoch 100 --warmUpIter 0 --batchsize 320 --freeze-bn --forgetRate 0.2 --out-dir ./finetune_ckpt/cifar10sn0.2_nested10_lr1e-3_bs320_freezeBN_fgr0.2_pre_nested10_10 --resumePthList ../nested/checkpoints/cifar10sn0.2_nested10_model1_Acc0.891_K9 ../nested/checkpoints/cifar10sn0.2_nested10_model3_Acc0.898_K8 --nested 10 --gpu 0
# CIFAR-100 Symmetric 20%, Nested=100
python3 main.py --train-dir ../data/CIFAR100/train_sn_0.2/ --val-dir ../data/CIFAR100/val/ --dataset CIFAR100 --lrSchedule 50 --nGradual 0 --lr 0.001 --nbEpoch 100 --warmUpIter 0 --batchsize 320 --freeze-bn --forgetRate 0.2 --out-dir ./finetune_ckpt/cifar100sn0.2_nested100_lr1e-3_bs320_freezeBN_fgr0.2_pre_nested100_100 --resumePthList ../nested/checkpoints/cifar100sn0.2_nested100_model1_Acc0.592_K46 ../nested/checkpoints/cifar100sn0.2_nested100_model3_Acc0.584_K41 --nested 100 --gpu 0The four Nested ResNet-18 networks trained in stage one can be downloaded here: CIFAR-10 ckpt1, CIFAR-10 ckpt2, CIFAR-100 ckpt1, CIFAR-100 ckpt2.
To evaluate models' ability of combating with label noise, we compute classification accuracy on a provided clean test set.
Evaluation of networks derived from stage one are provided here :
cd nested/
# for networks on Clothing1M
python3 test.py --test-dir ../data/Clothing1M/clean_test/ --dataset Clothing1M --arch resnet18 --resumePthList ./checkpoints/Cloth1M_nested100_lr2e-2_bs448_imgnet_freezeBN_model1_Acc0.735_K12 --KList 12 --gpu 0
# for networks on CIFAR-10
python3 test.py --test-dir ../data/CIFAR10/test/ --dataset CIFAR10 --arch resnet18 --resumePthList ./checkpoints/cifar10sn0.2_nested10_model1_Acc0.891_K9 --KList 9 --gpu 0More details can be found in nested/run_test.sh. Note that "_K12" in the model's name denotes the index of the optimal K, and the optimal number of channels for the model is actually 13 (nb of optimal channels = index of channel + 1).
Evaluation of networks derived from stage two are provided as follows.
- Networks trained on Clothing1M:
cd co_teaching_resnet/
python3 test.py --test-dir ../data/Clothing1M/clean_test/ --dataset Clothing1M --arch resnet18 --resumePthList ./finetune_ckpt/Cloth1M_nested100_lr2e-3_bs448_freezeBN_fgr0.3_pre_nested100_100_model2_Acc0.749_K24 --KList 24 --gpu 0More details can be found in co_teaching_resnet/run_test.sh.
- Networks trained on ANIMAL-10N:
cd co_teaching_vgg/
python3 test.py --test-dir ../data/Animal10N/test/ --dataset Animal10N --resumePthList ./finetune_ckpt/Animal10N_nested100_lr4e-3_bs128_freezeBN_fgr0.2_pre_nested100_100_nested100_100_model1_Acc0.842_K12 --KList 12 --gpu 0More details can be found in co_teaching_vgg/run_test.sh.
- Networks trained on CIFAR-10/CIFAR-100:
cd co_teaching_resnet/
# CIFAR-10
python3 test.py --test-dir ../data/CIFAR10/test/ --dataset CIFAR10 --arch resnet18 --resumePthList ./finetune_ckpt/cifar10sn0.2_nested10_lr1e-3_bs320_freezeBN_fgr0.2_pre_nested10_10_model2_Acc0.922_K11 --KList 11 --gpu 0
# CIFAR-100
python3 test.py --test-dir ../data/CIFAR100/test/ --dataset CIFAR100 --arch resnet18 --resumePthList ./finetune_ckpt/cifar100sn0.2_nested100_lr1e-3_bs320_freezeBN_fgr0.2_pre_nested100_100_model1_Acc0.669_K40 --KList 40 --gpu 0More details can be found in co_teaching_resnet/run_test.sh.