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07/31/2023
LaplaceConfidence: a Graph-based Approach for Learning with Noisy Labels
In real-world applications, perfect labels are rarely available, making ...
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06/03/2023
DOS: Diverse Outlier Sampling for Out-of-Distribution Detection
Modern neural networks are known to give overconfident prediction for ou...
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12/08/2022
MixBoost: Improving the Robustness of Deep Neural Networks by Boosting Data Augmentation
As more and more artificial intelligence (AI) technologies move from the...
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10/06/2022
Spatial-Temporal Graph Convolutional Gated Recurrent Network for Traffic Forecasting
As an important part of intelligent transportation systems, traffic fore...
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06/22/2022
Explanation-based Counterfactual Retraining(XCR): A Calibration Method for Black-box Models
With the rapid development of eXplainable Artificial Intelligence (XAI),...
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06/15/2022
READ: Aggregating Reconstruction Error into Out-of-distribution Detection
Detecting out-of-distribution (OOD) samples is crucial to the safe deplo...
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03/18/2022
Completing Partial Point Clouds with Outliers by Collaborative Completion and Segmentation
Most existing point cloud completion methods are only applicable to part...
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01/15/2022
Tailor Versatile Multi-modal Learning for Multi-label Emotion Recognition
Multi-modal Multi-label Emotion Recognition (MMER) aims to identify vari...
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12/06/2021
Two Wrongs Don't Make a Right: Combating Confirmation Bias in Learning with Label Noise
Noisy labels damage the performance of deep networks. For robust learnin...
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09/09/2021
Generation, augmentation, and alignment: A pseudo-source domain based method for source-free domain adaptation
Conventional unsupervised domain adaptation (UDA) methods need to access...
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08/10/2021
AdaRNN: Adaptive Learning and Forecasting of Time Series
Time series has wide applications in the real world and is known to be d...
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07/10/2021
Semi-Supervised Learning with Multi-Head Co-Training
Co-training, extended from self-training, is one of the frameworks for s...
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06/29/2021
Cross-domain error minimization for unsupervised domain adaptation
Unsupervised domain adaptation aims to transfer knowledge from a labeled...
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03/26/2020
Unsupervised domain adaptation with exploring more statistics and discriminative information
Unsupervised domain adaptation aims at transferring knowledge from the l...
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01/01/2020
Dual Adversarial Domain Adaptation
Unsupervised domain adaptation aims at transferring knowledge from the l...
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12/31/2019
Homogeneous Online Transfer Learning with Online Distribution Discrepancy Minimization
Transfer learning has been demonstrated to be successful and essential i...
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07/27/2019
