Changhwa Park

Selected Publications

1. Removing Undesirable Feature Contributions Using Out-of-Distribution Data

   Saehyung Lee, Changhwa Park, Hyungyu Lee, and 3 more authors

   In International Conference on Learning Representations 2021

   Abs Bib HTML

   Several data augmentation methods deploy unlabeled-in-distribution (UID) data to bridge the gap between the training and inference of neural networks. However, these methods have clear limitations in terms of availability of UID data and dependence of algorithms on pseudo-labels. Herein, we propose a data augmentation method to improve generalization in both adversarial and standard learning by using out-of-distribution (OOD) data that are devoid of the abovementioned issues. We show how to improve generalization theoretically using OOD data in each learning scenario and complement our theoretical analysis with experiments on CIFAR-10, CIFAR-100, and a subset of ImageNet. The results indicate that undesirable features are shared even among image data that seem to have little correlation from a human point of view. We also present the advantages of the proposed method through comparison with other data augmentation methods, which can be used in the absence of UID data. Furthermore, we demonstrate that the proposed method can further improve the existing state-of-the-art adversarial training.

   @inproceedings{lee2021removing,
     title = {Removing Undesirable Feature Contributions Using Out-of-Distribution Data},
     author = {Lee, Saehyung and Park, Changhwa and Lee, Hyungyu and Yi, Jihun and Lee, Jonghyun and Yoon, Sungroh},
     booktitle = {International Conference on Learning Representations},
     year = {2021},
     url = {https://openreview.net/forum?id=eIHYL6fpbkA},
   }

2. Mutual Learning for Long-Tailed Recognition

   Changhwa Park, Junho Yim, and Eunji Jun

   In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Jan 2023