Adaptive task sampling for meta-learning
Meta-learning methods have been extensively studied and applied in computer vision, especially for few-shot classification tasks. The key idea of meta-learning for few-shot classification is to mimic the few-shot situations faced at test time by randomly sampling classes in meta-training data to con...
Saved in:
Main Authors: | LIU, Chenghao, WANG, Zhihao, SAHOO, Doyen, FANG, Yuan, ZHANG, Kun, HOI, Steven C. H. |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2020
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/5293 https://ink.library.smu.edu.sg/context/sis_research/article/6296/viewcontent/ECCV20_GCP.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
Meta-transfer learning through hard tasks
by: SUN, Qianru, et al.
Published: (2022) -
MultiGPrompt for multi-task pre-training and prompting on graphs
by: YU, Xingtong, et al.
Published: (2024) -
FEW-SHOT IMAGE RECOGNITION AND OBJECT DETECTION
by: LI YITING
Published: (2023) -
Meta-transfer learning for few-shot learning
by: SUN, Qianru, et al.
Published: (2019) -
Few-shot learning in Wi-Fi-based indoor positioning
by: Xie, Feng, et al.
Published: (2024)