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pro vyhledávání: '"Ouyang, Yidong"'
Diffusion models, a specific type of generative model, have achieved unprecedented performance in recent years and consistently produce high-quality synthetic samples. A critical prerequisite for their notable success lies in the presence of a substa
Externí odkaz:
http://arxiv.org/abs/2405.16876
The diffusion model has shown remarkable performance in modeling data distributions and synthesizing data. However, the vanilla diffusion model requires complete or fully observed data for training. Incomplete data is a common issue in various real-w
Externí odkaz:
http://arxiv.org/abs/2307.00467
Synthetic data generation has become an emerging tool to help improve the adversarial robustness in classification tasks since robust learning requires a significantly larger amount of training samples compared with standard classification tasks. Amo
Externí odkaz:
http://arxiv.org/abs/2210.09643
Deep learning models have been widely applied in various aspects of daily life. Many variant models based on deep learning structures have achieved even better performances. Attention-based architectures have become almost ubiquitous in deep learning
Externí odkaz:
http://arxiv.org/abs/2202.12166
Autor:
Wang, Jindong, Lan, Cuiling, Liu, Chang, Ouyang, Yidong, Qin, Tao, Lu, Wang, Chen, Yiqiang, Zeng, Wenjun, Yu, Philip S.
Machine learning systems generally assume that the training and testing distributions are the same. To this end, a key requirement is to develop models that can generalize to unseen distributions. Domain generalization (DG), i.e., out-of-distribution
Externí odkaz:
http://arxiv.org/abs/2103.03097
Autor:
Guo, Weiyu, Ouyang, Yidong
Convolution neural networks have achieved remarkable performance in many tasks of computing vision. However, CNN tends to bias to low frequency components. They prioritize capturing low frequency patterns which lead them fail when suffering from appl
Externí odkaz:
http://arxiv.org/abs/2007.03244
Publikováno v:
In Neurocomputing 7 June 2022 489:167-178
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