Search Results for author: Xiaomin Li

Found 7 papers, 5 papers with code

StableIdentity: Inserting Anybody into Anywhere at First Sight

1 code implementation29 Jan 2024 Qinghe Wang, Xu Jia, Xiaomin Li, Taiqing Li, Liqian Ma, Yunzhi Zhuge, Huchuan Lu

We believe that the proposed StableIdentity is an important step to unify image, video, and 3D customized generation models.

BioDiffusion: A Versatile Diffusion Model for Biomedical Signal Synthesis

1 code implementation12 Jan 2024 Xiaomin Li, Mykhailo Sakevych, Gentry Atkinson, Vangelis Metsis

Leveraging these synthesized signals offers a notable solution to the aforementioned challenges.

Time Series

RCRN: Real-world Character Image Restoration Network via Skeleton Extraction

no code implementations16 Jul 2022 Daqian Shi, Xiaolei Diao, Hao Tang, Xiaomin Li, Hao Xing, Hao Xu

SENet aims to preserve the structural consistency of the character and normalize complex noise.

Image Restoration

TTS-CGAN: A Transformer Time-Series Conditional GAN for Biosignal Data Augmentation

1 code implementation28 Jun 2022 Xiaomin Li, Anne Hee Hiong Ngu, Vangelis Metsis

For time-series data, the suite of data augmentation strategies we can use to expand the size of the dataset is limited by the need to maintain the basic properties of the signal.

Data Augmentation Time Series +1

TTS-GAN: A Transformer-based Time-Series Generative Adversarial Network

2 code implementations6 Feb 2022 Xiaomin Li, Vangelis Metsis, Huangyingrui Wang, Anne Hee Hiong Ngu

For time-series, the suite of data augmentation tricks we can use to expand the size of the dataset is limited by the need to maintain the basic properties of the signal.

Data Augmentation Dimensionality Reduction +3

Artificial Intelligence-Driven Customized Manufacturing Factory: Key Technologies, Applications, and Challenges

no code implementations7 Aug 2021 Jiafu Wan, Xiaomin Li, Hong-Ning Dai, Andrew Kusiak, Miguel Martínez-García, Di Li

For that, Artificial Intelligence (AI) is enabling higher value-added manufacturing by accelerating the integration of manufacturing and information communication technologies, including computing, communication, and control.

Decision Making Edge-computing

Parallel Blockwise Knowledge Distillation for Deep Neural Network Compression

1 code implementation5 Dec 2020 Cody Blakeney, Xiaomin Li, Yan Yan, Ziliang Zong

The experimental results running on an AMD server with four Geforce RTX 2080Ti GPUs show that our algorithm can achieve 3x speedup plus 19% energy savings on VGG distillation, and 3. 5x speedup plus 29% energy savings on ResNet distillation, both with negligible accuracy loss.

Knowledge Distillation Neural Network Compression +3

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