Search Results for author:
Found 10 papers, 7 papers with code
Leveraging Gradients for Unsupervised Accuracy Estimation under Distribution Shift
Our key idea is that the model should be adjusted with a higher magnitude of gradients when it does not generalize to the test dataset with a distribution shift.
Mitigating Memorization of Noisy Labels by Clipping the Model Prediction
In the presence of noisy labels, designing robust loss functions is critical for securing the generalization performance of deep neural networks.
Open-Sampling: Exploring Out-of-Distribution data for Re-balancing Long-tailed datasets
Deep neural networks usually perform poorly when the training dataset suffers from extreme class imbalance.
ACIL: Analytic Class-Incremental Learning with Absolute Memorization and Privacy Protection
Class-incremental learning (CIL) learns a classification model with training data of different classes arising progressively.
Mitigating Neural Network Overconfidence with Logit Normalization
Our method is motivated by the analysis that the norm of the logit keeps increasing during training, leading to overconfident output.
GearNet: Stepwise Dual Learning for Weakly Supervised Domain Adaptation
Although there have been a few studies on this problem, most of them only exploit unidirectional relationships from the source domain to the target domain.
Open-sampling: Re-balancing Long-tailed Datasets with Out-of-Distribution Data
Deep neural networks usually perform poorly when the training dataset suffers from extreme class imbalance.
Automatic Online Multi-Source Domain Adaptation
Knowledge transfer across several streaming processes remain challenging problem not only because of different distributions of each stream but also because of rapidly changing and never-ending environments of data streams.
Open-set Label Noise Can Improve Robustness Against Inherent Label Noise
Learning with noisy labels is a practically challenging problem in weakly supervised learning.
ATL: Autonomous Knowledge Transfer from Many Streaming Processes
It automatically evolves its network structure from scratch with/without the presence of ground truth to overcome independent concept drifts in the source and target domain.
