Overview
This repository provides the implementation for the paper "Improved Regularization and Robustness for Fine-tuning in Neural Networks", which will be presented as a poster paper in NeurIPS'21.
In this work, we propose a regularized self-labeling approach that combines regularization and self-training methods for improving the generalization and robustness properties of fine-tuning. Our approach includes two components:
- First, we encode layer-wise regularization to penalize the model weights at different layers of the neural net.
- Second, we add self-labeling that relabels data points based on current neural net's belief and reweights data points whose confidence is low.
Requirements
To install requirements:
pip install -r requirements.txt
Data Preparation
We use seven image datasets in our paper. We list the link for downloading these datasets and describe how to prepare data to run our code below.
- Aircrafts: download and extract into
./data/aircrafts- remove the class
257.clutterout of the data directory
- remove the class
- CUB-200-2011: download and extract into
./data/CUB_200_2011/ - Caltech-256: download and extract into
./data/caltech256/ - Stanford-Cars: download and extract into
./data/StanfordCars/ - Stanford-Dogs: download and extract into
./data/StanfordDogs/ - Flowers: download and extract into
./data/flowers/ - MIT-Indoor: download and extract into
./data/Indoor/
Our code automatically handles the split of the datasets.
Usage
Our algorithm (RegSL) interpolates between layer-wise regularization and self-labeling. Run the following commands for conducting experiments in this paper.
Fine-tuning ResNet-101 on image classification tasks.
python train_constraint.py --model ResNet101 \
--config configs/config_constraint_indoor.json \
--reg_method constraint --reg_norm frob \
--reg_extractor 0.136809975858091 --reg_predictor 6.40780158171339 --scale_factor 2.52883770643206\
--device 1
python train_constraint.py --model ResNet101 \
--config configs/config_constraint_aircrafts.json \
--reg_method constraint --reg_norm frob \
--reg_extractor 1.18330556653284 --reg_predictor 5.27713618808711 --scale_factor 1.27679969876201\
--device 1
python train_constraint.py --model ResNet101 \
--config configs/config_constraint_birds.json \
--reg_method constraint --reg_norm frob \
--reg_extractor 0.204403908747731 --reg_predictor 23.7850606577679 --scale_factor 4.73803591794678\
--device 1
python train_constraint.py --model ResNet101 \
--config configs/config_constraint_caltech.json \
--reg_method constraint --reg_norm frob \
--reg_extractor 0.0867998872549272 --reg_predictor 9.4552942790218 --scale_factor 1.1785989596144\
--device 1
python train_constraint.py --model ResNet101 \
--config configs/config_constraint_cars.json \
--reg_method constraint --reg_norm frob \
--reg_extractor 1.3340347414257 --reg_predictor 8.26940794089601 --scale_factor 3.47676759842434\
--device 1
python train_constraint.py --model ResNet101 \
--config configs/config_constraint_dogs.json \
--reg_method constraint --reg_norm frob \
--reg_extractor 0.0561320847651626 --reg_predictor 4.46281825974388 --scale_factor 1.58722606909531\
--device 1
python train_constraint.py --model ResNet101 \
--config configs/config_constraint_flower.json \
--reg_method constraint --reg_norm frob \
--reg_extractor 0.131991042311165 --reg_predictor 10.7674132173309 --scale_factor 4.98010215976503\
--device 1
Fine-tuning ResNet-18 under label noise.
python train_label_noise.py --config configs/config_constraint_indoor.json --model ResNet18 \
--reg_method constraint --reg_norm frob \
--reg_extractor 7.80246991703043 --reg_predictor 14.077402847906 \
--noise_rate 0.2 --train_correct_label --reweight_epoch 5 --reweight_temp 2.0 --correct_epoch 10 --correct_thres 0.9
python train_label_noise.py --config configs/config_constraint_indoor.json --model ResNet18 \
--reg_method constraint --reg_norm frob \
--reg_extractor 8.47139398080791 --reg_predictor 19.0191127114923 \
--noise_rate 0.4 --train_correct_label --reweight_epoch 5 --reweight_temp 2.0 --correct_epoch 10 --correct_thres 0.9
python train_label_noise.py --config configs/config_constraint_indoor.json --model ResNet18 \
--reg_method constraint --reg_norm frob \
--reg_extractor 10.7576018531961 --reg_predictor 19.8157649727473 \
--noise_rate 0.6 --train_correct_label --reweight_epoch 5 --reweight_temp 2.0 --correct_epoch 10 --correct_thres 0.9
python train_label_noise.py --config configs/config_constraint_indoor.json --model ResNet18 \
--reg_method constraint --reg_norm frob \
--reg_extractor 9.2031662757248 --reg_predictor 6.41568500472423 \
--noise_rate 0.8 --train_correct_label --reweight_epoch 5 --reweight_temp 1.5 --correct_epoch 10 --correct_thres 0.9
Fine-tuning Vision Transformer on noisy labels.
python train_label_noise.py --config configs/config_constraint_indoor.json \
--model VisionTransformer --is_vit --img_size 224 --vit_type ViT-B_16 --vit_pretrained_dir pretrained/imagenet21k_ViT-B_16.npz \
--reg_method none --reg_norm none \
--lr 0.0001 --device 1 --noise_rate 0.4
python train_label_noise.py --config configs/config_constraint_indoor.json \
--model VisionTransformer --is_vit --img_size 224 --vit_type ViT-B_16 --vit_pretrained_dir pretrained/imagenet21k_ViT-B_16.npz \
--reg_method none --reg_norm none \
--lr 0.0001 --device 1 --noise_rate 0.8
python train_label_noise.py --config configs/config_constraint_indoor.json \
--model VisionTransformer --is_vit --img_size 224 --vit_type ViT-B_16 --vit_pretrained_dir pretrained/imagenet21k_ViT-B_16.npz \
--reg_method constraint --reg_norm frob \
--reg_extractor 0.7488074175044196 --reg_predictor 9.842955837419588 \
--train_correct_label --reweight_epoch 24 --correct_epoch 18\
--lr 0.0001 --device 1 --noise_rate 0.4
python train_label_noise.py --config configs/config_constraint_indoor.json \
--model VisionTransformer --is_vit --img_size 224 --vit_type ViT-B_16 --vit_pretrained_dir pretrained/imagenet21k_ViT-B_16.npz \
--reg_method constraint --reg_norm frob \
--reg_extractor 0.1568903647089986 --reg_predictor 1.407080880079702 \
--train_correct_label --reweight_epoch 18 --correct_epoch 2\
--lr 0.0001 --device 1 --noise_rate 0.8
Please follow the instructions in ViT-pytorch to download the pre-trained models.
Fine-tuning ResNet-18 on ChestX-ray14 data set.
Run experiments on ChestX-ray14 in reproduce-chexnet path:
cd reproduce-chexnet
python retrain.py --reg_method None --reg_norm None --device 0
python retrain.py --reg_method constraint --reg_norm frob \
--reg_extractor 5.728564437344309 --reg_predictor 2.5669480884876905 --scale_factor 1.0340072757925474 \
--device 0
Citation
If you find this repository useful, consider citing our work titled above.
Acknowledgment
Thanks to the authors of the following repositories for providing their implementation publicly available.
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