8 citations · 15 across the 8 of their papers we have counts for
8 papers
MixTTA: Low-Rank Cross-Channel Mixing for Reliable Test-Time Adaptation
Mansoo Jung, Youngwook Kim, Jungwoo Lee
Test-Time Adaptation (TTA) methods commonly update the affine parameters of normalization layers to adapt deployed models under distribution shifts. However, per-channel affine par…
Toward Reinforcement Learning-based Rectilinear Macro Placement Under Human Constraints
Tuyen P. Le, Hieu T. Nguyen, Seungyeol Baek +8
Macro placement is a critical phase in chip design, which becomes more intricate when involving general rectilinear macros and layout areas. Furthermore, macro placement that incor…
Domain-Aware Fine-Tuning: Enhancing Neural Network Adaptability
Seokhyeon Ha, Sunbeom Jung, Jungwoo Lee
Fine-tuning pre-trained neural network models has become a widely adopted approach across various domains. However, it can lead to the distortion of pre-trained feature extractors…
Learning to Learn Unlearned Feature for Brain Tumor Segmentation
Seungyub Han, Yeongmo Kim, Seokhyeon Ha +2
We propose a fine-tuning algorithm for brain tumor segmentation that needs only a few data samples and helps networks not to forget the original tasks. Our approach is based on act…
Bridging the Gap between Model Explanations in Partially Annotated Multi-label Classification
Youngwook Kim, Jae Myung Kim, Jieun Jeong +3
Due to the expensive costs of collecting labels in multi-label classification datasets, partially annotated multi-label classification has become an emerging field in computer visi…
Large Loss Matters in Weakly Supervised Multi-Label Classification
Youngwook Kim, Jae Myung Kim, Zeynep Akata +1
Weakly supervised multi-label classification (WSML) task, which is to learn a multi-label classification using partially observed labels per image, is becoming increasingly importa…
REST: Performance Improvement of a Black Box Model via RL-based Spatial Transformation
Jae Myung Kim, Hyungjin Kim, Chanwoo Park +1
In recent years, deep neural networks (DNN) have become a highly active area of research, and shown remarkable achievements on a variety of computer vision tasks. DNNs, however, ar…
Sampling-based Bayesian Inference with gradient uncertainty
Chanwoo Park, Jae Myung Kim, Seok Hyeon Ha +1
Deep neural networks(NNs) have achieved impressive performance, often exceed human performance on many computer vision tasks. However, one of the most challenging issues that still…