FineTune: Baseline method which simply updates parameters on new tasks.
EWC: Overcoming catastrophic forgetting in neural networks. PNAS2017 [paper]
LwF: Learning without Forgetting. ECCV2016 [paper]
Replay: Baseline method with exemplar replay.
GEM: Gradient Episodic Memory for Continual Learning. NIPS2017 [paper]
iCaRL: Incremental Classifier and Representation Learning. CVPR2017 [paper]
BiC: Large Scale Incremental Learning. CVPR2019 [paper]
WA: Maintaining Discrimination and Fairness in Class Incremental Learning. CVPR2020 [paper]
PODNet: PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning. ECCV2020 [paper]
DER: DER: Dynamically Expandable Representation for Class Incremental Learning. CVPR2021 [paper]
PASS: Prototype Augmentation and Self-Supervision for Incremental Learning. CVPR2021 [paper]
RMM: RMM: Reinforced Memory Management for Class-Incremental Learning. NeurIPS2021 [paper]
IL2A: Class-Incremental Learning via Dual Augmentation. NeurIPS2021 [paper]
SSRE: Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning. CVPR2022 [paper]
FeTrIL: Feature Translation for Exemplar-Free Class-Incremental Learning. WACV2023 [paper]
Coil: Co-Transport for Class-Incremental Learning. ACM MM2021 [paper]
FOSTER: Feature Boosting and Compression for Class-incremental Learning. ECCV 2022 [paper]
MEMO: A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning. ICLR 2023 Spotlight [paper]
BEEF: BEEF: Bi-Compatible Class-Incremental Learning via Energy-Based Expansion and Fusion. ICLR 2023 [paper]
SimpleCIL: Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You Need. arXiv 2023 [paper]