37 citations · 40 across the 3 of their papers we have counts for
6 papers
Interpolation-based semi-supervised learning for object detection
Jisoo Jeong, Vikas Verma, Minsung Hyun +2
Despite the data labeling cost for the object detection tasks being substantially more than that of the classification tasks, semi-supervised learning methods for object detection…
Class-Imbalanced Semi-Supervised Learning
Minsung Hyun, Jisoo Jeong, Nojun Kwak
Semi-Supervised Learning (SSL) has achieved great success in overcoming the difficulties of labeling and making full use of unlabeled data. However, SSL has a limited assumption th…
Disentangling Options with Hellinger Distance Regularizer
Minsung Hyun, Junyoung Choi, Nojun Kwak
In reinforcement learning (RL), temporal abstraction still remains as an important and unsolved problem. The options framework provided clues to temporal abstraction in the RL, and…
Feature Fusion for Online Mutual Knowledge Distillation
Jangho Kim, Minsung Hyun, Inseop Chung +1
We propose a learning framework named Feature Fusion Learning (FFL) that efficiently trains a powerful classifier through a fusion module which combines the feature maps generated…
Task-oriented Design through Deep Reinforcement Learning
Junyoung Choi, Minsung Hyun, Nojun Kwak
We propose a new low-cost machine-learning-based methodology which assists designers in reducing the gap between the problem and the solution in the design process. Our work applie…
Towards Governing Agent's Efficacy: Action-Conditional -VAE for Deep Transparent Reinforcement Learning
John Yang, Gyujeong Lee, Minsung Hyun +2
We tackle the blackbox issue of deep neural networks in the settings of reinforcement learning (RL) where neural agents learn towards maximizing reward gains in an uncontrollable w…