no code implementations • 18 Mar 2024 • Seungbeom Woo, Geonwoo Baek, TaeHoon Kim, Jaemin Na, Joong-won Hwang, Wonjun Hwang
This framework dynamically cycles through multiple target domains, aligning each domain individually to restrain the biased alignment problem, and utilizes Fisher information to minimize the forgetting of knowledge from previous target domains.
no code implementations • 18 Mar 2024 • Jisu Han, Jaemin Na, Wonjun Hwang
Continual learning aims to refine model parameters for new tasks while retaining knowledge from previous tasks.
1 code implementation • 14 Mar 2024 • Dinh Phat Do, TaeHoon Kim, Jaemin Na, Jiwon Kim, Keonho Lee, Kyunghwan Cho, Wonjun Hwang
However, there are limited studies on adapting from the visible to the thermal domain, because the domain gap between the visible and thermal domains is much larger than expected, and traditional domain adaptation can not successfully facilitate learning in this situation.
no code implementations • 9 May 2023 • Jisu Han, Jaemin Na, Wonjun Hwang
We also propose a method to selectively interpolate the weight of the previous model for a balance between stability and plasticity, and we adjust whether to transfer through model confidence to ensure the performance of the previous class and enable exploratory learning.
1 code implementation • 26 Nov 2021 • Jaemin Na, Dongyoon Han, Hyung Jin Chang, Wonjun Hwang
In the contrastive space, inter-domain discrepancy is mitigated by constraining instances to have contrastive views and labels, and the consensus space reduces the confusion between intra-domain categories.
Ranked #1 on Unsupervised Domain Adaptation on PACS
1 code implementation • CVPR 2021 • Jaemin Na, Heechul Jung, Hyung Jin Chang, Wonjun Hwang
However, most of the studies were based on direct adaptation from the source domain to the target domain and have suffered from large domain discrepancies.
Ranked #6 on Domain Adaptation on Office-31
1 code implementation • ICCV 2021 • Wonchul Son, Jaemin Na, Junyong Choi, Wonjun Hwang
Specifically, when teaching a smaller teacher assistant at the next step, the existing larger teacher assistants from the previous step are used as well as the teacher network.