EAT: Towards Long-Tailed Out-of-Distribution Detection

PyTorch Code for the following paper at AAAI2024 arXiv

Title: Expand and Augment: Towards Long-Tailed Out-of-Distribution Detection

Abstract Despite recent promising results on out-of-distribution (OOD) detection, most existing works assume that the in-distribution training dataset is class-balanced, which rarely holds in the wild. In this paper, we study the challenging task of long-tailed OOD detection in which the in-distribution data follows a long-tailed class distribution. The goal is to not only reliably detect OOD data but also achieve high classification accuracy on in-distribution data. We approach these goals by developing a simple and efficient method: (1) it expands the in-distribution class space with mul-tiple abstention OOD classes to build a detector with clear decision boundaries; (2) it augments the context-limited tail classes by pasting images onto the context-rich head class and OOD images to improve the generalization. Our method, EAT (Expand and AugmenT), can displace the outlier exposure method for dealing with long-tailed datasets and can be used as an add-on for existing methods to boost the generalization of tail classes. Extensive experiments show that EAT outperforms the previous state-of-the-art approach by an average of 2.0% OOD detection AUROC and 2.9% in-distribution classification accuracy on various benchmark datasets.

Stage 1 training: Training the whole model using EAT framework

CIFAR10-LT:

python stage1.py --gpu 0 --epochs 180 --lr 1e-3 --ds cifar10  \
    --Lambda0 0.05 --Lambda1 0.05 --rho 0.01  --md ResNet18  --odc 3 --num_ood_samples 300000 \
	--drp <where_you_store_all_your_datasets> --srp <where_to_save_the_ckpt>

CIFAR100-LT:

python stage1.py --gpu 0 --epochs 180 --lr 1e-3 --ds cifar100  \
    --Lambda0 0.05 --Lambda1 0.05 --rho 0.01  --md ResNet18  --odc 30 --num_ood_samples 300000 \
	--drp <where_you_store_all_your_datasets> --srp <where_to_save_the_ckpt>

ImageNet-LT:

python stage1.py --gpu 0 --epochs 100 --ds imagenet  \
    --md ResNet50 -e 60 --opt sgd --decay multisteps --lr 0.1 --wd 5e-5 --tb 100 \
    --ddp --dist_url tcp://localhost:23457 \
    --Lambda0 0.05 --Lambda1 0.05 -momentum 0.9 --odc 30 \
	--drp <where_you_store_all_your_datasets> --srp <where_to_save_the_ckpt>

Stage 2 training: Finetune the classifier

CIFAR10-LT:

python stage2.py --gpu 0 --ds cifar10 \
    --odc 3 --epochs 10 --rho 0.01 \
	--drp <where_you_store_all_your_datasets> \
	--pretrained_exp_str <the_name_of_your_stage1_training_experiment>

CIFAR100-LT:

python stage2.py --gpu 0 --ds cifar100 \
    --odc 30 --epochs 10 --rho 0.01 \
	--drp <where_you_store_all_your_datasets> \
	--pretrained_exp_str <the_name_of_your_stage1_training_experiment>

ImageNet-LT:

python stage2.py --gpu 1 --ds imagenet -e 1 --opt sgd --decay multisteps --lr 0.01 --wd 5e-5 --tb 100 \
    --ddp --dist_url tcp://localhost:23457 --odc 30 --md ResNet50 --momentum 0 \
	--pretrained_exp_str <the_name_of_your_stage1_training_experiment>

--pretrained_exp_str should be something like e180-b128-adam-lr0.001-wd0.0005-cos_odc3-Lambda00.05-Lambda10.05-tau0

Testing

CIFAR10-LT:

for dout in texture svhn cifar tin lsun places365
do
python test.py --gpu 0 --ds cifar10 --odc 3 --dout $dout \
	--drp <where_you_store_all_your_datasets> \
	--ckpt_path <where_you_save_the_ckpt>
done

CIFAR100-LT:

for dout in texture svhn cifar tin lsun places365
do
python test.py --gpu 0 --ds cifar100 --odc 30 --dout $dout \
	--drp <where_you_store_all_your_datasets> \
	--ckpt_path <where_you_save_the_ckpt>
done

ImageNet-LT:

python test_imagenet.py --gpu 0 --odc 30 \
	--drp <where_you_store_all_your_datasets> \
	--ckpt_path <where_you_save_the_ckpt>

Datasets download

Since our experimental setup refers to previous papers, we have provided links below to our reference projects to download the relevant datasets.

See this link to download relevant datasets such as "tinyimages80m"： https://github.com/hendrycks/outlier-exposure

See this link to download and process the OOD data set for ImageNet-LT: https://github.com/amazon-science/long-tailed-ood-detection

Citation

If you find this repo useful for your work, please cite as:

@article{wei2024EAT,
  title={EAT: Towards Long-Tailed Out-of-Distribution Detection},
  author={Wei, Tong and Wang, Bo-Lin and Zhang, Min-Ling},
  journal={Proceedings of the 38th AAAI Conference on Artificial Intelligence},
  year={2024}
}