.. _Methods Reproduced:

Methods Reproduced
=====================

-  ``FineTune``: Baseline method which simply updates parameters on new tasks.
-  ``EWC``: Overcoming catastrophic forgetting in neural networks. PNAS2017 `[paper] <https://arxiv.org/abs/1612.00796>`_
-  ``LwF``:  Learning without Forgetting. ECCV2016 `[paper] <https://arxiv.org/abs/1606.09282>`_
-  ``Replay``: Baseline method with exemplar replay.
-  ``GEM``: Gradient Episodic Memory for Continual Learning. NIPS2017 `[paper] <https://arxiv.org/abs/1706.08840>`_
-  ``iCaRL``: Incremental Classifier and Representation Learning. CVPR2017 `[paper] <https://arxiv.org/abs/1611.07725>`_
-  ``BiC``: Large Scale Incremental Learning. CVPR2019 `[paper] <https://arxiv.org/abs/1905.13260>`_
-  ``WA``: Maintaining Discrimination and Fairness in Class Incremental Learning. CVPR2020 `[paper] <https://arxiv.org/abs/1911.07053>`_
-  ``PODNet``: PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning. ECCV2020 `[paper] <https://arxiv.org/abs/2004.13513>`_
-  ``DER``: DER: Dynamically Expandable Representation for Class Incremental Learning. CVPR2021 `[paper] <https://arxiv.org/abs/2103.16788>`_
-  ``PASS``: Prototype Augmentation and Self-Supervision for Incremental Learning. CVPR2021 `[paper] <https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_Prototype_Augmentation_and_Self-Supervision_for_Incremental_Learning_CVPR_2021_paper.pdf>`_
-  ``RMM``: RMM: Reinforced Memory Management for Class-Incremental Learning. NeurIPS2021 `[paper] <https://proceedings.neurips.cc/paper/2021/hash/1cbcaa5abbb6b70f378a3a03d0c26386-Abstract.html>`_
-  ``IL2A``: Class-Incremental Learning via Dual Augmentation. NeurIPS2021 `[paper] <https://proceedings.neurips.cc/paper/2021/file/77ee3bc58ce560b86c2b59363281e914-Paper.pdf>`_
-  ``SSRE``: Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning. CVPR2022 `[paper] <https://arxiv.org/abs/2203.06359>`_
-  ``FeTrIL``: Feature Translation for Exemplar-Free Class-Incremental Learning. WACV2023 `[paper] <https://arxiv.org/abs/2211.13131>`_
-  ``Coil``: Co-Transport for Class-Incremental Learning. ACM MM2021 `[paper] <https://arxiv.org/abs/2107.12654>`_
-  ``FOSTER``: Feature Boosting and Compression for Class-incremental Learning. ECCV 2022 `[paper] <https://arxiv.org/abs/2204.04662>`_
-  ``MEMO``: A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning. ICLR 2023 Spotlight `[paper] <https://openreview.net/forum?id=S07feAlQHgM>`_
-  ``BEEF``: BEEF: Bi-Compatible Class-Incremental Learning via Energy-Based Expansion and Fusion. ICLR 2023 `[paper] <https://openreview.net/forum?id=iP77_axu0h3>`_
-  ``SimpleCIL``: Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You Need. arXiv 2023 `[paper] <https://arxiv.org/abs/2303.07338>`_