no code implementations • 22 Nov 2023 • Seongyoon Kim, Gihun Lee, Jaehoon Oh, Se-Young Yun
Additionally, we observe that as data heterogeneity increases, the gap between higher feature norms for observed classes, obtained from local models, and feature norms of unobserved classes widens, in contrast to the behavior of classifier weight norms.
1 code implementation • 1 Nov 2023 • Taehyeon Kim, Joonkee Kim, Gihun Lee, Se-Young Yun
Notably, utilizing 'opposite' as the noisy instruction in ID, which exhibits the maximum divergence from the original instruction, consistently produces the most significant performance gains across multiple models and tasks.
no code implementations • 24 Aug 2023 • Gihun Lee, Minchan Jeong, Sangmook Kim, Jaehoon Oh, Se-Young Yun
FedSOL is designed to identify gradients of local objectives that are inherently orthogonal to directions affecting the proximal objective.
no code implementations • 10 Aug 2023 • Jun Ma, Ronald Xie, Shamini Ayyadhury, Cheng Ge, Anubha Gupta, Ritu Gupta, Song Gu, Yao Zhang, Gihun Lee, Joonkee Kim, Wei Lou, Haofeng Li, Eric Upschulte, Timo Dickscheid, José Guilherme de Almeida, Yixin Wang, Lin Han, Xin Yang, Marco Labagnara, Vojislav Gligorovski, Maxime Scheder, Sahand Jamal Rahi, Carly Kempster, Alice Pollitt, Leon Espinosa, Tâm Mignot, Jan Moritz Middeke, Jan-Niklas Eckardt, Wangkai Li, Zhaoyang Li, Xiaochen Cai, Bizhe Bai, Noah F. Greenwald, David Van Valen, Erin Weisbart, Beth A. Cimini, Trevor Cheung, Oscar Brück, Gary D. Bader, Bo wang
This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging.
2 code implementations • 7 Dec 2022 • Gihun Lee, Sangmook Kim, Joonkee Kim, Se-Young Yun
Cell segmentation is a fundamental task for computational biology analysis.
no code implementations • 29 Sep 2021 • Sangmin Bae, Sungnyun Kim, Jongwoo Ko, Gihun Lee, Seungjong Noh, Se-Young Yun
This paper proposes a novel contrastive learning framework, called Self-Contrastive (SelfCon) Learning, that self-contrasts within multiple outputs from the different levels of a multi-exit network.
1 code implementation • 29 Jun 2021 • Sangmin Bae, Sungnyun Kim, Jongwoo Ko, Gihun Lee, Seungjong Noh, Se-Young Yun
To this end, we propose Self-Contrastive (SelfCon) learning, which self-contrasts within multiple outputs from the different levels of a single network.
2 code implementations • 6 Jun 2021 • Gihun Lee, Minchan Jeong, Yongjin Shin, Sangmin Bae, Se-Young Yun
In federated learning, a strong global model is collaboratively learned by aggregating clients' locally trained models.
1 code implementation • 13 Oct 2020 • Sungnyun Kim, Gihun Lee, Sangmin Bae, Se-Young Yun
Contrastive learning has shown remarkable results in recent self-supervised approaches for visual representation.
1 code implementation • 24 Apr 2020 • Gihun Lee, Sangmin Bae, Jaehoon Oh, Se-Young Yun
With the success of deep learning in various fields and the advent of numerous Internet of Things (IoT) devices, it is essential to lighten models suitable for low-power devices.