Preserving Earlier Knowledge in Continual Learning with the Help of All Previous Feature Extractors

28 April 2021

Papers citing "Preserving Earlier Knowledge in Continual Learning with the Help of All Previous Feature Extractors"

7 / 7 papers shown

Title
Neural Collapse Terminus: A Unified Solution for Class Incremental Learning and Its Variants Yibo Yang Haobo Yuan Hefei Ling Jianlong Wu Lefei Zhang Zhouchen Lin Philip Torr Dacheng Tao Guohao Li CLL 31 8 0 03 Aug 2023
DeCoR: Defy Knowledge Forgetting by Predicting Earlier Audio Codes Xilin Jiang Yinghao Aaron Li N. Mesgarani CLL 24 1 0 29 May 2023
How Efficient Are Today's Continual Learning Algorithms? Md Yousuf Harun Jhair Gallardo Tyler L. Hayes Christopher Kanan 24 24 0 29 Mar 2023
CLIP model is an Efficient Continual Learner Vishal G. Thengane Salman Khan Munawar Hayat Fahad Shahbaz Khan BDL VLM CLL 112 46 0 06 Oct 2022
S-Prompts Learning with Pre-trained Transformers: An Occam's Razor for Domain Incremental Learning Yabin Wang Zhiwu Huang Xiaopeng Hong CLL VLM 27 213 0 26 Jul 2022
A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning Da-Wei Zhou Qiwen Wang Han-Jia Ye De-Chuan Zhan 29 123 0 26 May 2022
DyTox: Transformers for Continual Learning with DYnamic TOken eXpansion Arthur Douillard Alexandre Ramé Guillaume Couairon Matthieu Cord CLL 30 295 0 22 Nov 2021