23 citations · 72 across the 11 of their papers we have counts for
11 papers
Multi-Label Knowledge Distillation
Penghui Yang, Ming-Kun Xie, Chen-Chen Zong +4
Existing knowledge distillation methods typically work by imparting the knowledge of output logits or intermediate feature maps from the teacher network to the student network, whi…
SDFReg: Learning Signed Distance Functions for Point Cloud Registration
Leida Zhang, Zhengda Lu, Kai Liu +1
Learning-based point cloud registration methods can handle clean point clouds well, while it is still challenging to generalize to noisy, partial, and density-varying point clouds.…
On the Importance of Feature Separability in Predicting Out-Of-Distribution Error
Renchunzi Xie, Hongxin Wei, Lei Feng +2
Estimating the generalization performance is practically challenging on out-of-distribution (OOD) data without ground-truth labels. While previous methods emphasize the connection…
Can Adversarial Training Be Manipulated By Non-Robust Features?
Lue Tao, Lei Feng, Hongxin Wei +3
Adversarial training, originally designed to resist test-time adversarial examples, has shown to be promising in mitigating training-time availability attacks. This defense ability…
Multi-Class Classification from Single-Class Data with Confidences
Yuzhou Cao, Lei Feng, Senlin Shu +4
Can we learn a multi-class classifier from only data of a single class? We show that without any assumptions on the loss functions, models, and optimizers, we can successfully lear…
On the Robustness of Average Losses for Partial-Label Learning
Jiaqi Lv, Biao Liu, Lei Feng +6
Partial-label learning (PLL) utilizes instances with PLs, where a PL includes several candidate labels but only one is the true label (TL). In PLL, identification-based strategy (I…
MetaInfoNet: Learning Task-Guided Information for Sample Reweighting
Hongxin Wei, Lei Feng, Rundong Wang +1
Deep neural networks have been shown to easily overfit to biased training data with label noise or class imbalance. Meta-learning algorithms are commonly designed to alleviate this…
SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning
Zhuowei Wang, Jing Jiang, Bo Han +4
Deep learning with noisy labels is a challenging task. Recent prominent methods that build on a specific sample selection (SS) strategy and a specific semi-supervised learning (SSL…
GLIMG: Global and Local Item Graphs for Top-N Recommender Systems
Zhuoyi Lin, Lei Feng, Rui Yin +2
Graph-based recommendation models work well for top-N recommender systems due to their capability to capture the potential relationships between entities. However, most of the exis…
Learning with Multiple Complementary Labels
Lei Feng, Takuo Kaneko, Bo Han +3
A complementary label (CL) simply indicates an incorrect class of an example, but learning with CLs results in multi-class classifiers that can predict the correct class. Unfortuna…
A distribution-free smoothed combination method of biomarkers to improve diagnostic accuracy in multi-category classification
Raju Maiti, Jialiang Li, Priyam Das +3
Results from multiple diagnostic tests are usually combined to improve the overall diagnostic accuracy. For binary classification, maximization of the empirical estimate of the are…