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pro vyhledávání: '"Lee Dongheon"'
SoRA: Singular Value Decomposed Low-Rank Adaptation for Domain Generalizable Representation Learning
Domain generalization (DG) aims to adapt a model using one or multiple source domains to ensure robust performance in unseen target domains. Recently, Parameter-Efficient Fine-Tuning (PEFT) of foundation models has shown promising results in the cont
Externí odkaz:
http://arxiv.org/abs/2412.04077
Autor:
Lee, Dongheon, Choi, Jung-Woo
This paper presents a framework for universal sound separation and polyphonic audio classification, addressing the challenges of separating and classifying individual sound sources in a multichannel mixture. The proposed framework, DeFT-Mamba, utiliz
Externí odkaz:
http://arxiv.org/abs/2409.12413
For Image Super-Resolution (SR), it is common to train and evaluate scale-specific models composed of an encoder and upsampler for each targeted scale. Consequently, many SR studies encounter substantial training times and complex deployment requirem
Externí odkaz:
http://arxiv.org/abs/2408.09674
Transformer, composed of self-attention and Feed-Forward Network, has revolutionized the landscape of network design across various vision tasks. FFN is a versatile operator seamlessly integrated into nearly all AI models to effectively harness rich
Externí odkaz:
http://arxiv.org/abs/2406.02021
Recently, in the super-resolution (SR) domain, transformers have outperformed CNNs with fewer FLOPs and fewer parameters since they can deal with long-range dependency and adaptively adjust weights based on instance. In this paper, we demonstrate tha
Externí odkaz:
http://arxiv.org/abs/2404.11848
Numerical models have long been used to understand geoscientific phenomena, including tidal currents, crucial for renewable energy production and coastal engineering. However, their computational cost hinders generating data of varying resolutions. A
Externí odkaz:
http://arxiv.org/abs/2401.15893
Autor:
Lee, Dongheon, Choi, Jung-Woo
In this work, we present DeFTAN-II, an efficient multichannel speech enhancement model based on transformer architecture and subgroup processing. Despite the success of transformers in speech enhancement, they face challenges in capturing local relat
Externí odkaz:
http://arxiv.org/abs/2308.15777
Autor:
Lee, Dongheon, Choi, Jung-Woo
Publikováno v:
IEEE Signal Processing Letters, Vol. 30, pp. 155-159, 2023
In this study, we propose a dense frequency-time attentive network (DeFT-AN) for multichannel speech enhancement. DeFT-AN is a mask estimation network that predicts a complex spectral masking pattern for suppressing the noise and reverberation embedd
Externí odkaz:
http://arxiv.org/abs/2212.07570
Autor:
Lee, Wonkyeong, Wagner, Fabian, Galdran, Adrian, Shi, Yongyi, Xia, Wenjun, Wang, Ge, Mou, Xuanqin, Ahamed, Md. Atik, Imran, Abdullah Al Zubaer, Oh, Ji Eun, Kim, Kyungsang, Baek, Jong Tak, Lee, Dongheon, Hong, Boohwi, Tempelman, Philip, Lyu, Donghang, Kuiper, Adrian, van Blokland, Lars, Calisto, Maria Baldeon, Hsieh, Scott, Han, Minah, Baek, Jongduk, Maier, Andreas, Wang, Adam, Gold, Garry Evan, Choi, Jang-Hwan
Publikováno v:
In Medical Image Analysis January 2025 99
Publikováno v:
In Food Research International November 2024 196