5 papers
Frequency-Aware Token Reduction for Efficient Vision Transformer
Dong-Jae Lee, Jiwan Hur, Jaehyun Choi +2
Vision Transformers have demonstrated exceptional performance across various computer vision tasks, yet their quadratic computational complexity concerning token length remains a s…
SynAD: Enhancing Real-World End-to-End Autonomous Driving Models through Synthetic Data Integration
Jongsuk Kim, Jaeyoung Lee, Gyojin Han +3
Recent advancements in deep learning and the availability of high-quality real-world driving datasets have propelled end-to-end autonomous driving. Despite this progress, relying s…
DAM: Domain-Aware Module for Multi-Domain Dataset Condensation
Jaehyun Choi, Gyojin Han, Dong-Jae Lee +2
Dataset Condensation (DC) has emerged as a promising solution to mitigate the computational and storage burdens associated with training deep learning models. However, existing DC…
Self-supervised Transformation Learning for Equivariant Representations
Jaemyung Yu, Jaehyun Choi, Dong-Jae Lee +2
Unsupervised representation learning has significantly advanced various machine learning tasks. In the computer vision domain, state-of-the-art approaches utilize transformations l…
AH-OCDA: Amplitude-based Curriculum Learning and Hopfield Segmentation Model for Open Compound Domain Adaptation
Jaehyun Choi, Junwon Ko, Dong-Jae Lee +1
Open compound domain adaptation (OCDA) is a practical domain adaptation problem that consists of a source domain, target compound domain, and unseen open domain. In this problem, t…