Instance-Level Embedding Adaptation for Few-Shot Learning
Few-shot learning aims to recognize novel categories from just a few labeled instances. Existing metric learning-based approaches perform classifications by nearest neighbor search in the embedding space. The embedding function is a deep neural network and usually shared by all novel categories. How...
Main Authors: | Fusheng Hao, Jun Cheng, Lei Wang, Jianzhong Cao |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2019-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8672561/ |
Similar Items
-
Graph-Based Domain Adaptation Few-Shot Learning for Hyperspectral Image Classification
by: Yanbing Xu, et al.
Published: (2023-02-01) -
Few-Shot SAR-ATR Based on Instance-Aware Transformer
by: Xin Zhao, et al.
Published: (2022-04-01) -
Active Instance Selection for Few-Shot Classification
by: Junsup Shin, et al.
Published: (2022-01-01) -
Filtering Specialized Change in a Few-Shot Setting
by: Martin Hermann, et al.
Published: (2023-01-01) -
TAE-Net: Task-Adaptive Embedding Network for Few-Shot Remote Sensing Scene Classification
by: Wendong Huang, et al.
Published: (2021-12-01)