Search Results for author: Jae-Seok Choi

Found 7 papers, 1 papers with code

KL-Divergence-Based Region Proposal Network for Object Detection

no code implementations22 May 2020 Geonseok Seo, Jaeyoung Yoo, Jae-Seok Choi, Nojun Kwak

The learning of the region proposal in object detection using the deep neural networks (DNN) is divided into two tasks: binary classification and bounding box regression task.

Binary Classification Object +3

S3: A Spectral-Spatial Structure Loss for Pan-Sharpening Networks

no code implementations13 Jun 2019 Jae-Seok Choi, Yongwoo Kim, Munchurl Kim

Our proposed S3 loss can be very effectively utilized for pan-sharpening with various types of CNN structures, resulting in significant visual improvements on PS images with suppressed artifacts.

Genetic-Gated Networks for Deep Reinforcement

no code implementations26 Nov 2018 Simyung Chang, John Yang, Jae-Seok Choi, Nojun Kwak

We introduce the Genetic-Gated Networks (G2Ns), simple neural networks that combine a gate vector composed of binary genetic genes in the hidden layer(s) of networks.

reinforcement-learning Reinforcement Learning (RL)

Single Image Super-Resolution Using Lightweight CNN with Maxout Units

no code implementations7 Nov 2017 Jae-Seok Choi, Munchurl Kim

To the best of our knowledge, we are the first to incorporate MU into SR applications and show promising performance results.

Image Super-Resolution

BOOK: Storing Algorithm-Invariant Episodes for Deep Reinforcement Learning

no code implementations5 Sep 2017 Simyung Chang, Youngjoon Yoo, Jae-Seok Choi, Nojun Kwak

Our method learns hundreds to thousand times faster than the conventional methods by learning only a handful of core cluster information, which shows that deep RL agents can effectively learn through the shared knowledge from other agents.

Imitation Learning reinforcement-learning +1

Residual Features and Unified Prediction Network for Single Stage Detection

1 code implementation17 Jul 2017 Kyoungmin Lee, Jae-Seok Choi, Jisoo Jeong, Nojun Kwak

They are much faster than two stage detectors that use region proposal networks (RPN) without much degradation in the detection performances.

Region Proposal

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