Code for ICLR 2024 Spotlight Paper "Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks"
ImageNet-1K Fully-Supervised Models
python src/finetune.py --seed=3907 --train-dataset=CIFAR10 --eval-datasets=CIFAR10 --model=resnet50_noise_5 --model_source=timm --epochs=30 --lr=0.01 --wd=0.0001 --batch-size=64 --save SAVE_PATH --results SAVE_PATH/results.jsonl --data-location=DATA_DIR --freeze-encoder --template=simple_template --load-zero-shot-classifier False --num-shots=None
CLIP Models
python src/finetune.py --seed=3907 --train-dataset=CIFAR10 --eval-datasets=CIFAR10 --model=RN50_yfcc15m_orig --model_source=open_clip --epochs=30 --lr=0.01 --wd=0.0001 --batch-size=64 --save SAVE_PATH --results SAVE_PATH/results.jsonl --data-location=DATA_DIR --freeze-encoder --template=simple_template --load-zero-shot-classifier False --num-shots=None
ImageNet-1K Fully-Supervised Models
python src/nml_tune.py --seed=3907 --train-dataset=CIFAR10 --eval-datasets=CIFAR10 --model=resnet50_noise_5 --model_source=timm --epochs=30 --lr=0.001 --wd=0.0001 --batch-size=64 --save SAVE_PATH --results SAVE_PATH/results.jsonl --data-location=DATA_DIR --template=openai_imagenet_template --freeze-encoder --load-zero-shot-classifier False --use-weak-strong=False --mlp-with-res=True --mlp-res-scale-init=1.0 --mlp-after-ratio=4 --mlp-after-layers=1 --mlp-after-layers=1 --mse-loss-weight=1.0 --cov-loss-weight=0.04 --svd-loss-weight=0.001
CLIP Models
python src/nml_tune.py --seed=3907 --train-dataset=CIFAR10 --eval-datasets=CIFAR10 --model=RN50_yfcc15m_noise_30 --model_source=open_clip --epochs=30 --lr=0.001 --wd=0.0001 --batch-size=64 --save SAVE_PATH --results SAVE_PATH/results.jsonl --data-location=DATA_DIR --template=openai_imagenet_template --freeze-encoder --load-zero-shot-classifier False --use-weak-strong=False --mlp-with-res=True --mlp-res-scale-init=1.0 --mlp-after-ratio=4 --mlp-after-layers=1 --mlp-after-layers=1 --mse-loss-weight=1.0 --cov-loss-weight=0.04 --svd-loss-weight=0.001
Our pre-trained models (noisy) are released by by request. You can also pre-train your own models on noisy datasets using timm and open_clip. Once you have the pre-trained models, replace/create the model config as in:
@inproceedings{
chen2024understanding,
title={Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks},
author={Hao Chen and Jindong Wang and Ankit Shah and Ran Tao and Hongxin Wei and Xing Xie and Masashi Sugiyama and Bhiksha Raj},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
}
Most of our code are developed based on timm, open_clip, and wise-ft
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