A Simple Baseline that Questions the Use of Pretrained-Models in Continual Learning

10 October 2022

Papers citing "A Simple Baseline that Questions the Use of Pretrained-Models in Continual Learning"

8 / 8 papers shown

Title
FeNeC: Enhancing Continual Learning via Feature Clustering with Neighbor- or Logit-Based Classification Kamil Książek Hubert Jastrzębski Bartosz Trojan Krzysztof Pniaczek Michał Karp Jacek Tabor CLL 76 0 0 18 Mar 2025
Capture Global Feature Statistics for One-Shot Federated Learning Zenghao Guan Yucan Zhou Xiaoyan Gu FedML 63 0 0 10 Mar 2025
Future-Proofing Class-Incremental Learning Quentin Jodelet Xin Liu Yin Jun Phua Tsuyoshi Murata VLM 36 2 0 04 Apr 2024
Read Between the Layers: Leveraging Multi-Layer Representations for Rehearsal-Free Continual Learning with Pre-Trained Models Kyra Ahrens Hans Hergen Lehmann Jae Hee Lee Stefan Wermter CLL 35 7 0 13 Dec 2023
Rethinking Class-incremental Learning in the Era of Large Pre-trained Models via Test-Time Adaptation Imad Eddine Marouf Subhankar Roy Enzo Tartaglione Stéphane Lathuilière CLL 16 3 0 17 Oct 2023
ImageNet-21K Pretraining for the Masses T. Ridnik Emanuel Ben-Baruch Asaf Noy Lihi Zelnik-Manor SSeg VLM CLIP 179 686 0 22 Apr 2021
Adversarial Continual Learning Sayna Ebrahimi Franziska Meier Roberto Calandra Trevor Darrell Marcus Rohrbach CLL VLM 152 198 0 21 Mar 2020
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 296 39,194 0 01 Sep 2014