Awesome

t-vMF Similarity

The Pytorch implementation for the CVPR2021 paper of "t-vMF Similarity for Regularizing In-Class Feature Distribution" by Takumi Kobayashi.

Citation

If you find our project useful in your research, please cite it as follows:

@inproceedings{kobayashi2021cvpr,
  title={t-vMF Similarity for Regularizing In-Class Feature Distribution},
  author={Takumi Kobayashi},
  booktitle={Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2021}
}

Introduction

This work proposes a method to effectively impose regularization on feature representation learning. By focusing on the angle between a feature and a classifier which is embedded in cosine similarity at the classification layer, we formulate a novel similarity beyond the cosine based on von Mises-Fisher distribution of directional statistics. In contrast to the cosine similarity, our similarity is compact while having heavy tail, which contributes to regularizing intra-class feature distribution to improve generalization performance. The method works in the classification loss in stead of cosine similarity without adding regularization loss, to improve feature representation trained on ill-conditioned datasets, such as imbalanced, small-scaled and noisy-labeled datasets. It can also improve performance on large-scale healthy dataset such as ImageNet. For more detail, please refer to our paper.

Usage

Training

The algorithm is simply implemented in the class tvMFLoss at models/Loss.py; the core is found in line 36 of models/Loss.py.

For example, the ResNet-10 equipped with the t-vMF similarity is trained from scratch on ImageNet-LT dataset by

CUDA_VISIBLE_DEVICES=0,1 python main_train.py --dataset imagenetlt  --data ./datasets/imagenetlt --net-config ResNet10Feature  --loss-config tvMFLoss_k16 --out-dir ./results/imagenetlt/ResNet10Feature/tvMFLoss_k16/ --workers 12 --seed 0 

CUDA_VISIBLE_DEVICES=0,1 python main_finetune.py --dataset imagenetlt  --data ./datasets/imagenetlt --net-config ResNet10Feature_finetune  --loss-config CosLoss --model-file ./results/imagenetlt/ResNet10Feature/tvMFLoss_k16//model_best.pth.tar --out-dir ./results/imagenetlt/ResNet10Feature/tvMFLoss_k16/finetune --workers 12 --seed 0

Note that the ImageNet-LT dataset must be downloaded at ./datasets/imagenetlt/ before the training and we follow the imbalance-aware training procedure presented in [1]; the standard training is performed by main_train.py and then fine-tuning via class-imbalance aware sampling is done by main_finetune.py.

Or, in short, you can simply run the script by

sh do_imbalance.sh 0,1 imagenetlt ResNet10Feature tvMFLoss_k16

Results

ImageNet

References

[1] Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng and Yannis Kalantidis. "DECOUPLING REPRESENTATION AND CLASSIFIER FOR LONG-TAILED RECOGNITION." In ICLR, 2020.

[2] Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga and Tengyu Ma. "Learning imbalanced datasets with label-distribution-aware margin loss." In NeurIPS, 2019.

Contact

takumi.kobayashi (At) aist.go.jp

Acknowledgement

The class-wise sampler utils/ClassAwareSampler.py is from the Classifier-Balancing.