research
∙
09/08/2023
UER: A Heuristic Bias Addressing Approach for Online Continual Learning
Online continual learning aims to continuously train neural networks fro...
share
research
∙
07/31/2023
MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learning
Equipping a deep model the abaility of few-shot learning, i.e., learning...
research
∙
04/10/2023
PCR: Proxy-based Contrastive Replay for Online Class-Incremental Continual Learning
Online class-incremental continual learning is a specific task of contin...
research
∙
03/03/2022
MetaDT: Meta Decision Tree for Interpretable Few-Shot Learning
Few-Shot Learning (FSL) is a challenging task, which aims to recognize n...
research
∙
10/09/2021
SGMNet: Scene Graph Matching Network for Few-Shot Remote Sensing Scene Classification
Few-Shot Remote Sensing Scene Classification (FSRSSC) is an important ta...
research
∙
08/11/2021
Prototype Completion for Few-Shot Learning
Few-shot learning aims to recognize novel classes with few examples. Pre...
research
∙
03/26/2021
MetaNODE: Prototype Optimization as a Neural ODE for Few-Shot Learning
Few-Shot Learning (FSL) is a challenging task, i.e., how to recognize no...
research
∙
09/10/2020
Prototype Completion with Primitive Knowledge for Few-Shot Learning
Few-shot learning is a challenging task, which aims to learn a classifie...
research
∙
07/05/2020