no code implementations • 19 Apr 2023 • Seongho Joe, Byoungjip Kim, Hoyoung Kang, Kyoungwon Park, Bogun Kim, Jaeseon Park, Joonseok Lee, Youngjune Gwon
The recent advances in representation learning inspire us to take on the challenging problem of unsupervised image classification tasks in a principled way.
no code implementations • 7 Jan 2023 • Byoungjip Kim, Sungik Choi, Dasol Hwang, Moontae Lee, Honglak Lee
Despite surprising performance on zero-shot transfer, pre-training a large-scale multimodal model is often prohibitive as it requires a huge amount of data and computing resources.
no code implementations • 16 Jan 2021 • Byoungjip Kim, Jinho Choo, Yeong-Dae Kwon, Seongho Joe, Seungjai Min, Youngjune Gwon
This paper introduces SelfMatch, a semi-supervised learning method that combines the power of contrastive self-supervised learning and consistency regularization.
2 code implementations • NeurIPS 2020 • Yeong-Dae Kwon, Jinho Choo, Byoungjip Kim, Iljoo Yoon, Youngjune Gwon, Seungjai Min
We introduce Policy Optimization with Multiple Optima (POMO), an end-to-end approach for building such a heuristic solver.
no code implementations • CVPR 2021 • Jongwon Choi, Kwang Moo Yi, Ji-Hoon Kim, Jinho Choo, Byoungjip Kim, Jin-Yeop Chang, Youngjune Gwon, Hyung Jin Chang
We show that our method can be applied to classification tasks on multiple different datasets -- including one that is a real-world dataset with heavy data imbalance -- significantly outperforming the state of the art.
1 code implementation • 4 Mar 2020 • Yonghyun Jeong, Hyunjin Choi, Byoungjip Kim, Youngjune Gwon
We propose DefogGAN, a generative approach to the problem of inferring state information hidden in the fog of war for real-time strategy (RTS) games.
no code implementations • 31 Jul 2017 • Taeksoo Kim, Byoungjip Kim, Moonsu Cha, Jiwon Kim
To address the issue, we propose an unsupervised method to learn to transfer visual attribute.