no code implementations • 31 Jul 2023 • Mingcai Chen, Yuntao Du, Wei Tang, Baoming Zhang, Hao Cheng, Shuwei Qian, Chongjun Wang
We introduce LaplaceConfidence, a method that to obtain label confidence (i. e., clean probabilities) utilizing the Laplacian energy.
1 code implementation • 29 Jul 2023 • Mingcai Chen, Yu Zhao, Zhonghuang Wang, Bing He, Jianhua Yao
Immune repertoire classification, a typical multiple instance learning (MIL) problem, is a frontier research topic in computational biology that makes transformative contributions to new vaccines and immune therapies.
1 code implementation • 3 Jun 2023 • Wenyu Jiang, Hao Cheng, Mingcai Chen, Chongjun Wang, Hongxin Wei
Modern neural networks are known to give overconfident prediction for out-of-distribution inputs when deployed in the open world.
no code implementations • 16 Nov 2022 • Mingcai Chen, Yu Zhao, Bing He, Zongbo Han, Bingzhe Wu, Jianhua Yao
Then, we refurbish the noisy labels using the estimated clean probabilities and the pseudo-labels from the model's predictions.
1 code implementation • 6 Oct 2022 • Le Zhao, Mingcai Chen, Yuntao Du, Haiyang Yang, Chongjun Wang
We design an attention module to capture long-term dependency by mining periodic information in traffic data.
no code implementations • 15 Jun 2022 • Wenyu Jiang, Yuxin Ge, Hao Cheng, Mingcai Chen, Shuai Feng, Chongjun Wang
We propose a novel method, READ (Reconstruction Error Aggregated Detector), to unify inconsistencies from classifier and autoencoder.
Out-of-Distribution Detection Out of Distribution (OOD) Detection
no code implementations • 6 Dec 2021 • Mingcai Chen, Hao Cheng, Yuntao Du, Ming Xu, Wenyu Jiang, Chongjun Wang
We show that our method successfully alleviates the damage of both label noise and confirmation bias.
Ranked #2 on Image Classification on mini WebVision 1.0
no code implementations • 9 Sep 2021 • Yuntao Du, Haiyang Yang, Mingcai Chen, Juan Jiang, Hongtao Luo, Chongjun Wang
The proposed method firstly generates and augments the pseudo-source domain, and then employs distribution alignment with four novel losses based on pseudo-label based strategy.
1 code implementation • 10 Jul 2021 • Mingcai Chen, Yuntao Du, Yi Zhang, Shuwei Qian, Chongjun Wang
Co-training, extended from self-training, is one of the frameworks for semi-supervised learning.