Inverse design of organic light-emitting diode structure based on deep neural networks

Autor: Kim Sanmun, Shin Jeong Min, Lee Jaeho, Park Chanhyung, Lee Songju, Park Juho, Seo Dongjin, Park Sehong, Park Chan Y., Jang Min Seok

Publikováno v: Nanophotonics, Vol 10, Iss 18, Pp 4533-4541 (2021)

The optical properties of thin-film light emitting diodes (LEDs) are strongly dependent on their structures due to light interference inside the devices. However, the complexity of the design space grows exponentially with the number of design parame

Externí odkaz: https://doaj.org/article/bc049cbc8f49424ab8dead14590449dc

Constructing a Singing Style Caption Dataset

Autor: Ok, Hyunjong, Lee, Jaeho

Singing voice synthesis and conversion have emerged as significant subdomains of voice generation, leading to much demands on prompt-conditioned generation. Unlike common voice data, generating a singing voice requires an understanding of various ass

Externí odkaz: http://arxiv.org/abs/2409.09866

Towards Federated Low-Rank Adaptation with Rank-Heterogeneous Communication

Autor: Byun, Yuji, Lee, Jaeho

Low-rank adaptation (LoRA) is an attractive alternative of adapting full weights for the federated fine-tuning of large pretrained models, which can significantly reduce the memory and communication burden. In principle, federated LoRA can provide an

Externí odkaz: http://arxiv.org/abs/2406.17477

Rethinking Pruning Large Language Models: Benefits and Pitfalls of Reconstruction Error Minimization

Autor: Shin, Sungbin, Park, Wonpyo, Lee, Jaeho, Lee, Namhoon

This work suggests fundamentally rethinking the current practice of pruning large language models (LLMs). The way it is done is by divide and conquer: split the model into submodels, sequentially prune them, and reconstruct predictions of the dense c

Externí odkaz: http://arxiv.org/abs/2406.15524

NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results

Autor: Liu, Xiaoning, Wu, Zongwei, Li, Ao, Vasluianu, Florin-Alexandru, Zhang, Yulun, Gu, Shuhang, Zhang, Le, Zhu, Ce, Timofte, Radu, Jin, Zhi, Wu, Hongjun, Wang, Chenxi, Ling, Haitao, Cai, Yuanhao, Bian, Hao, Zheng, Yuxin, Lin, Jing, Yuille, Alan, Shao, Ben, Guo, Jin, Liu, Tianli, Wu, Mohao, Feng, Yixu, Hou, Shuo, Lin, Haotian, Zhu, Yu, Wu, Peng, Dong, Wei, Sun, Jinqiu, Zhang, Yanning, Yan, Qingsen, Zou, Wenbin, Yang, Weipeng, Li, Yunxiang, Wei, Qiaomu, Ye, Tian, Chen, Sixiang, Zhang, Zhao, Zhao, Suiyi, Wang, Bo, Luo, Yan, Zuo, Zhichao, Wang, Mingshen, Wang, Junhu, Wei, Yanyan, Sun, Xiaopeng, Gao, Yu, Huang, Jiancheng, Chen, Hongming, Chen, Xiang, Tang, Hui, Chen, Yuanbin, Zhou, Yuanbo, Dai, Xinwei, Qiu, Xintao, Deng, Wei, Gao, Qinquan, Tong, Tong, Li, Mingjia, Hu, Jin, He, Xinyu, Guo, Xiaojie, Sabarinathan, Uma, K, Sasithradevi, A, Bama, B Sathya, Roomi, S. Mohamed Mansoor, Srivatsav, V., Wang, Jinjuan, Sun, Long, Chen, Qiuying, Shao, Jiahong, Zhang, Yizhi, Conde, Marcos V., Feijoo, Daniel, Benito, Juan C., García, Alvaro, Lee, Jaeho, Kim, Seongwan, A, Sharif S M, Khujaev, Nodirkhuja, Tsoy, Roman, Murtaza, Ali, Khairuddin, Uswah, Faudzi, Ahmad 'Athif Mohd, Malagi, Sampada, Joshi, Amogh, Akalwadi, Nikhil, Desai, Chaitra, Tabib, Ramesh Ashok, Mudenagudi, Uma, Lian, Wenyi, Lian, Wenjing, Kalyanshetti, Jagadeesh, Aralikatti, Vijayalaxmi Ashok, Yashaswini, Palani, Upasi, Nitish, Hegde, Dikshit, Patil, Ujwala, C, Sujata, Yan, Xingzhuo, Hao, Wei, Fu, Minghan, choksy, Pooja, Sarvaiya, Anjali, Upla, Kishor, Raja, Kiran, Yan, Hailong, Zhang, Yunkai, Li, Baiang, Zhang, Jingyi, Zheng, Huan

This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this challenge is to discover an effective network design or solution capable of generating brighter, clearer, and vi

Externí odkaz: http://arxiv.org/abs/2404.14248