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20212023
most citedAddressing Negative Transfer in Diffusion Models

2 citations · 2 across the 6 of their papers we have counts for

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6 papers

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★ 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…

cs.CL2022

RWEN-TTS: Relation-aware Word Encoding Network for Natural Text-to-Speech Synthesis

Shinhyeok Oh, HyeongRae Noh, Yoonseok Hong +1

With the advent of deep learning, a huge number of text-to-speech (TTS) models which produce human-like speech have emerged. Recently, by introducing syntactic and semantic informa…

cs.CL2021

Netmarble AI Center's WMT21 Automatic Post-Editing Shared Task Submission

Shinhyeok Oh, Sion Jang, Hu Xu +2

This paper describes Netmarble's submission to WMT21 Automatic Post-Editing (APE) Shared Task for the English-German language pair. First, we propose a Curriculum Training Strategy…