v0.1

This is the first Github release of PyCIL. Reproduced methods are listed as:

• FineTune: Baseline method which simply updates parameters on new tasks, suffering from Catastrophic Forgetting. By default, weights corresponding to the outputs of previous classes are not updated.
• EWC: Overcoming catastrophic forgetting in neural networks. PNAS2017 [paper]
• LwF: Learning without Forgetting. ECCV2016 [paper]
• Replay: Baseline method with exemplars.
• 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]

Stay tuned for more state-of-the-arts in PyCIL!