40 citations · 135 across the 12 of their papers we have counts for
16 papers · 1 filter
Bayesian Optimization Meets Self-Distillation
HyunJae Lee, Heon Song, Hyeonsoo Lee +3
Bayesian optimization (BO) has contributed greatly to improving model performance by suggesting promising hyperparameter configurations iteratively based on observations from multi…
Benchmarking Self-Supervised Learning on Diverse Pathology Datasets
Mingu Kang, Heon Song, Seonwook Park +2
Computational pathology can lead to saving human lives, but models are annotation hungry and pathology images are notoriously expensive to annotate. Self-supervised learning has sh…
Improving Multi-fidelity Optimization with a Recurring Learning Rate for Hyperparameter Tuning
HyunJae Lee, Gihyeon Lee, Junhwan Kim +3
Despite the evolution of Convolutional Neural Networks (CNNs), their performance is surprisingly dependent on the choice of hyperparameters. However, it remains challenging to effi…
Interactive Multi-Class Tiny-Object Detection
Chunggi Lee, Seonwook Park, Heon Song +5
Annotating tens or hundreds of tiny objects in a given image is laborious yet crucial for a multitude of Computer Vision tasks. Such imagery typically contains objects from various…
Learning Visual Context by Comparison
Minchul Kim, Jongchan Park, Seil Na +2
Finding diseases from an X-ray image is an important yet highly challenging task. Current methods for solving this task exploit various characteristics of the chest X-ray image, bu…
Reducing Domain Gap by Reducing Style Bias
Hyeonseob Nam, HyunJae Lee, Jongchan Park +2
Convolutional Neural Networks (CNNs) often fail to maintain their performance when they confront new test domains, which is known as the problem of domain shift. Recent studies sug…
Visuomotor Understanding for Representation Learning of Driving Scenes
Seokju Lee, Junsik Kim, Tae-Hyun Oh +4
Dashboard cameras capture a tremendous amount of driving scene video each day. These videos are purposefully coupled with vehicle sensing data, such as from the speedometer and ine…
PseudoEdgeNet: Nuclei Segmentation only with Point Annotations
Inwan Yoo, Donggeun Yoo, Kyunghyun Paeng
Nuclei segmentation is one of the important tasks for whole slide image analysis in digital pathology. With the drastic advance of deep learning, recent deep networks have demonstr…
Learning Loss for Active Learning
Donggeun Yoo, In So Kweon
The performance of deep neural networks improves with more annotated data. The problem is that the budget for annotation is limited. One solution to this is active learning, where…
Distort-and-Recover: Color Enhancement using Deep Reinforcement Learning
Jongchan Park, Joon-Young Lee, Donggeun Yoo +1
Learning-based color enhancement approaches typically learn to map from input images to retouched images. Most of existing methods require expensive pairs of input-retouched images…
Learning Image Representations by Completing Damaged Jigsaw Puzzles
Dahun Kim, Donghyeon Cho, Donggeun Yoo +1
In this paper, we explore methods of complicating self-supervised tasks for representation learning. That is, we do severe damage to data and encourage a network to recover them. F…
Two-Phase Learning for Weakly Supervised Object Localization
Dahun Kim, Donghyeon Cho, Donggeun Yoo +1
Weakly supervised semantic segmentation and localiza- tion have a problem of focusing only on the most important parts of an image since they use only image-level annota- tions. In…
Action-Driven Object Detection with Top-Down Visual Attentions
Donggeun Yoo, Sunggyun Park, Kyunghyun Paeng +2
A dominant paradigm for deep learning based object detection relies on a "bottom-up" approach using "passive" scoring of class agnostic proposals. These approaches are efficient bu…
Pixel-Level Domain Transfer
Donggeun Yoo, Namil Kim, Sunggyun Park +2
We present an image-conditional image generation model. The model transfers an input domain to a target domain in semantic level, and generates the target image in pixel level. To…
AttentionNet: Aggregating Weak Directions for Accurate Object Detection
Donggeun Yoo, Sunggyun Park, Joon-Young Lee +2
We present a novel detection method using a deep convolutional neural network (CNN), named AttentionNet. We cast an object detection problem as an iterative classification problem,…
Fisher Kernel for Deep Neural Activations
Donggeun Yoo, Sunggyun Park, Joon-Young Lee +1
Compared to image representation based on low-level local descriptors, deep neural activations of Convolutional Neural Networks (CNNs) are richer in mid-level representation, but p…