Jun-Yeong Moon — accepted AI-conference papers

2024

Towards Model-Agnostic Dataset Condensation by Heterogeneous Models

Jun-Yeong Moon, Jung Uk Kim*, Gyeong-Moon Park*

ECCV 2024oral

"The advancement of deep learning has coincided with the proliferation of both models and available data. The surge in dataset sizes and the subsequent surge in computational requirements have led to the development of the Dataset Condensation (DC). While prior studies have delved into generating sy…

Cited by 1 Source ↗PDF ↗code ↗Scholar ↗

2023

Online Class Incremental Learning on Stochastic Blurry Task Boundary via Mask and Visual Prompt Tuning

Jun-Yeong Moon, Keon-Hee Park, Jung Uk Kim, Gyeong-Moon Park

ICCV 2023poster

Continual learning aims to learn a model from a continuous stream of data, but it mainly assumes a fixed number of data and tasks with clear task boundaries. However, in real-world scenarios, the number of input data and tasks is constantly changing in a statistical way, not a static way. Although r…

Cited by 27 PDF ↗code ↗Scholar ↗