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20212026
most citedAddressing Negative Transfer in Diffusion Models

2 citations · 5 across the 25 of their papers we have counts for

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cs.CV2023

HarmonyView: Harmonizing Consistency and Diversity in One-Image-to-3D

Sangmin Woo, Byeongjun Park, Hyojun Go +2

Recent progress in single-image 3D generation highlights the importance of multi-view coherency, leveraging 3D priors from large-scale diffusion models pretrained on Internet-scale…

cs.CV2023

Denoising Task Routing for Diffusion Models

Byeongjun Park, Sangmin Woo, Hyojun Go +2

Diffusion models generate highly realistic images by learning a multi-step denoising process, naturally embodying the principles of multi-task learning (MTL). Despite the inherent…

cs.CV2023

Multi-Architecture Multi-Expert Diffusion Models

Yunsung Lee, Jin-Young Kim, Hyojun Go +3

In this paper, we address the performance degradation of efficient diffusion models by introducing Multi-architecturE Multi-Expert diffusion models (MEME). We identify the need for…

cs.CV2023

ScoreCL: Augmentation-Adaptive Contrastive Learning via Score-Matching Function

Jin-Young Kim, Soonwoo Kwon, Hyojun Go +3

Self-supervised contrastive learning (CL) has achieved state-of-the-art performance in representation learning by minimizing the distance between positive pairs while maximizing th…

cs.CV2023★ 2 cited

Addressing Negative Transfer in Diffusion Models

Hyojun Go, JinYoung Kim, Yunsung Lee +4

Diffusion-based generative models have achieved remarkable success in various domains. It trains a shared model on denoising tasks that encompass different noise levels simultaneou…

cs.CL2023

Cross Encoding as Augmentation: Towards Effective Educational Text Classification

Hyun Seung Lee, Seungtaek Choi, Yunsung Lee +5

Text classification in education, usually called auto-tagging, is the automated process of assigning relevant tags to educational content, such as questions and textbooks. However,…

cs.CL2023

Evaluation of Question Generation Needs More References

Shinhyeok Oh, Hyojun Go, Hyeongdon Moon +4

Question generation (QG) is the task of generating a valid and fluent question based on a given context and the target answer. According to various purposes, even given the same co…