FSCIL-EACA: Few-Shot Class-Incremental Learning Network Based on Embedding Augmentation and Classifier Adaptation for Image Classification-Reference-Cited by-同舟云学术

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FSCIL-EACA: Few-Shot Class-Incremental Learning Network Based on Embedding Augmentation and Classifier Adaptation for Image Classification

Published:2024-01 Issue:1 Volume:33 Page:139-152
ISSN:2075-5597
Container-title:Chinese Journal of Electronics
language:
Short-container-title:Chinese J. Elect.

Author:

Zhang Ruru¹,Haihong E¹,Song Meina¹

Affiliation:

1. School of Computer Science, Beijing University of Posts and Telecommunications,Beijing,China,100876

Funder

National Science Foundation of China

Beijing Natural Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9970761/10410587/10410591.pdf?arnumber=10410591

Reference54 articles.

1. Few-Shot Open-Set Recognition of Hyperspectral Images with Outlier Calibration Network

2. Meta-MSNet: Meta-Learning Based Multi-Source Data Fusion for Traffic Flow Prediction

3. Fine-Grained Image Classification With Global Information and Adaptive Compensation Loss

4. Learning Placeholders for Open-Set Recognition

5. Learning to Classify With Incremental New Class

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