8 citations · 15 across the 8 of their papers we have counts for
4 papers · 1 filter
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…