research
∙
06/14/2023
Improving Generalization in Meta-Learning via Meta-Gradient Augmentation
Meta-learning methods typically follow a two-loop framework, where each ...
share
research
∙
03/11/2023
MetaViewer: Towards A Unified Multi-View Representation
Existing multi-view representation learning methods typically follow a s...
research
∙
03/04/2023
Fine-Grained Classification with Noisy Labels
Learning with noisy labels (LNL) aims to ensure model generalization giv...
research
∙
08/24/2022
Self-Filtering: A Noise-Aware Sample Selection for Label Noise with Confidence Penalization
Sample selection is an effective strategy to mitigate the effect of labe...
research
∙
11/08/2021
Learning to Rectify for Robust Learning with Noisy Labels
Label noise significantly degrades the generalization ability of deep mo...
research
∙
08/13/2021
Learning Transferable Parameters for Unsupervised Domain Adaptation
Unsupervised domain adaptation (UDA) enables a learning machine to adapt...
research
∙
05/14/2021
Attentional Prototype Inference for Few-Shot Semantic Segmentation
This paper aims to address few-shot semantic segmentation. While existin...
research
∙
05/08/2021
MetaKernel: Learning Variational Random Features with Limited Labels
Few-shot learning deals with the fundamental and challenging problem of ...
research
∙
12/23/2020
Direct Estimation of Spinal Cobb Angles by Structured Multi-Output Regression
The Cobb angle that quantitatively evaluates the spinal curvature plays ...
research
∙
06/11/2020
Learning to Learn Kernels with Variational Random Features
In this work, we introduce kernels with random Fourier features in the m...
research
∙
08/21/2019