135 citations · 198 across the 7 of their papers we have counts for
7 papers
ContraCluster: Learning to Classify without Labels by Contrastive Self-Supervision and Prototype-Based Semi-Supervision
Seongho Joe, Byoungjip Kim, Hoyoung Kang +5
The recent advances in representation learning inspire us to take on the challenging problem of unsupervised image classification tasks in a principled way. We propose ContraCluste…
Transferring Pre-trained Multimodal Representations with Cross-modal Similarity Matching
Byoungjip Kim, Sungik Choi, Dasol Hwang +2
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.…
SelfMatch: Combining Contrastive Self-Supervision and Consistency for Semi-Supervised Learning
Byoungjip Kim, Jinho Choo, Yeong-Dae Kwon +3
This paper introduces SelfMatch, a semi-supervised learning method that combines the power of contrastive self-supervised learning and consistency regularization. SelfMatch consist…
POMO: Policy Optimization with Multiple Optima for Reinforcement Learning
Yeong-Dae Kwon, Jinho Choo, Byoungjip Kim +3
In neural combinatorial optimization (CO), reinforcement learning (RL) can turn a deep neural net into a fast, powerful heuristic solver of NP-hard problems. This approach has a gr…
DefogGAN: Predicting Hidden Information in the StarCraft Fog of War with Generative Adversarial Nets
Yonghyun Jeong, Hyunjin Choi, Byoungjip Kim +1
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. Given a partially observed sta…
VaB-AL: Incorporating Class Imbalance and Difficulty with Variational Bayes for Active Learning
Jongwon Choi, Kwang Moo Yi, Jihoon Kim +5
Active Learning for discriminative models has largely been studied with the focus on individual samples, with less emphasis on how classes are distributed or which classes are hard…
Unsupervised Visual Attribute Transfer with Reconfigurable Generative Adversarial Networks
Taeksoo Kim, Byoungjip Kim, Moonsu Cha +1
Learning to transfer visual attributes requires supervision dataset. Corresponding images with varying attribute values with the same identity are required for learning the transfe…