Distributed few-shot learning with prototype distribution correction-Reference-Cited by-同舟云学术

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Distributed few-shot learning with prototype distribution correction

Published:2023-11-20 Issue:24 Volume:53 Page:30552-30565
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Fu Zhiling,Tang Dongfang,Ma Pingchuan,Wang Zhe,Gao Wen

Funder

Shanghai Science and Technology Program “Distributed and generative few-shot algorithm and theory research”

Natural Science Foundation of Jilin Province

Shanghai Science and Technology Program “Federated based cross-domain and cross-task incremental learning”

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-023-05103-4.pdf

Reference55 articles.

1. Byerly A, Kalganova T, Ott R (2022) The current state of the art in deep learning for image classification: a review. In: Science and information conference, pp 88–105

2. La Rosa B, Capobianco R, Nardi D (2023) A self-interpretable module for deep image classification on small data. Appl Intell 53(8):9115–9147

3. Xu C, Gao W, Li T, Bai N, Li G, Zhang Y (2023) Teacher-student collaborative knowledge distillation for image classification. Appl Intell 53(2):1997–2009

4. Song Y, Wang T, Cai P, Mondal SK, Sahoo JP (2023) A comprehensive survey of few-shot learning: evolution, applications, challenges, and opportunities. ACM Comput Surv

5. Li X, Yang X, Ma Z, Xue J-H (2023) Deep metric learning for few-shot image classification: a review of recent developments. Pattern Recognit 109381

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