Frequency Domain-based Dataset Distillation

1 code implementation • NeurIPS 2023 • DongHyeok Shin, Seungjae Shin, Il-Chul Moon

This paper presents FreD, a novel parameterization method for dataset distillation, which utilizes the frequency domain to distill a small-sized synthetic dataset from a large-sized original dataset.

Loss-Curvature Matching for Dataset Selection and Condensation

1 code implementation • 8 Mar 2023 • Seungjae Shin, HeeSun Bae, DongHyeok Shin, Weonyoung Joo, Il-Chul Moon

Training neural networks on a large dataset requires substantial computational costs.

Unknown-Aware Domain Adversarial Learning for Open-Set Domain Adaptation

1 code implementation • 15 Jun 2022 • JoonHo Jang, Byeonghu Na, DongHyeok Shin, Mingi Ji, Kyungwoo Song, Il-Chul Moon

Therefore, we propose Unknown-Aware Domain Adversarial Learning (UADAL), which $\textit{aligns}$ the source and the target-$\textit{known}$ distribution while simultaneously $\textit{segregating}$ the target-$\textit{unknown}$ distribution in the feature alignment procedure.

Domain Adaptation