Search Results for author: Jeesoo Kim

Found 8 papers, 3 papers with code

Smoothing the Generative Latent Space with Mixup-based Distance Learning

no code implementations23 Nov 2021 Chaerin Kong, Jeesoo Kim, Donghoon Han, Nojun Kwak

Producing diverse and realistic images with generative models such as GANs typically requires large scale training with vast amount of images.

Normalization Matters in Weakly Supervised Object Localization

1 code implementation ICCV 2021 Jeesoo Kim, Junsuk Choe, Sangdoo Yun, Nojun Kwak

Weakly-supervised object localization (WSOL) enables finding an object using a dataset without any localization information.

Weakly-Supervised Object Localization

A Comprehensive Overhaul of Feature Distillation

2 code implementations ICCV 2019 Byeongho Heo, Jeesoo Kim, Sangdoo Yun, Hyojin Park, Nojun Kwak, Jin Young Choi

We investigate the design aspects of feature distillation methods achieving network compression and propose a novel feature distillation method in which the distillation loss is designed to make a synergy among various aspects: teacher transform, student transform, distillation feature position and distance function.

General Classification Knowledge Distillation +2

HC-Net: Memory-based Incremental Dual-Network System for Continual learning

no code implementations27 Sep 2018 Jangho Kim, Jeesoo Kim, Nojun Kwak

The C-Net guarantees no degradation in the performance of the previously learned tasks and the H-Net shows high confidence in finding the origin of an input sample.

Continual Learning Hippocampus

StackNet: Stacking Parameters for Continual learning

no code implementations7 Sep 2018 Jangho Kim, Jeesoo Kim, Nojun Kwak

The StackNet guarantees no degradation in the performance of the previously learned tasks and the index module shows high confidence in finding the origin of an input sample.

Continual Learning

Vehicle Image Generation Going Well with The Surroundings

no code implementations9 Jul 2018 Jeesoo Kim, Jangho Kim, Jaeyoung Yoo, Daesik Kim, Nojun Kwak

Using a subnetwork based on a precedent work of image completion, our model makes the shape of an object.

Colorization Image Generation +4

Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams

no code implementations CVPR 2018 Daesik Kim, Youngjoon Yoo, Jeesoo Kim, Sangkuk Lee, Nojun Kwak

In this work, we introduce a new algorithm for analyzing a diagram, which contains visual and textual information in an abstract and integrated way.

Graph Generation Question Answering

Cannot find the paper you are looking for? You can Submit a new open access paper.