3 papers
cs.CV2025
Rethinking Direct Preference Optimization in Diffusion Models
Junyong Kang, Seohyun Lim, Kyungjune Baek +1
Aligning text-to-image (T2I) diffusion models with human preferences has emerged as a critical research challenge. While recent advances in this area have extended preference optim…
cs.RO2025
Disentangled Multi-Context Meta-Learning: Unlocking robust and Generalized Task Learning
Seonsoo Kim, Jun-Gill Kang, Taehong Kim +1
In meta-learning and its downstream tasks, many methods rely on implicit adaptation to task variations, where multiple factors are mixed together in a single entangled representati…
cs.CL2024
In-Context Learning with Noisy Labels
Junyong Kang, Donghyun Son, Hwanjun Song +1
In-context learning refers to the emerging ability of large language models (LLMs) to perform a target task without additional training, utilizing demonstrations of the task. Recen…