Do Pre-trained Models Benefit Equally in Continual Learning?-Reference-Cited by-同舟云学术

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Do Pre-trained Models Benefit Equally in Continual Learning?

Published:2023-01 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
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Author:

Lee Kuan-Ying¹,Zhong Yuanyi¹,Wang Yu-Xiong¹

Affiliation:

1. University of Illinois at Urbana-Champaign

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10030081/10030084/10030430.pdf?arnumber=10030430

Reference44 articles.

1. A continual learning survey: Defying forgetting in classification tasks;de lange;TPAMI,2021

2. Continual learning with deep generative replay;shin;NeurIPS,2017

3. GAN memory with no forgetting;cong;NeurIPS,2020

4. Progressive neural networks;rusu,2016

5. ImageNet: A large-scale hierarchical image database

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Adaptive Robot Traversability Estimation Based on Self-Supervised Online Continual Learning in Unstructured Environments;IEEE Robotics and Automation Letters;2024-06

2. Online Class Incremental Learning with One-Vs-All Classifiers for Resource Constrained Devices;2023 International Symposium on Image and Signal Processing and Analysis (ISPA);2023-09-18

3. Recent Advances in Class-Incremental Learning;Lecture Notes in Computer Science;2023

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