Mammoth - An Extendible (General) Continual Learning Framework for Pytorch

Official repository of Class-Incremental Continual Learning into the eXtended DER-verse and Dark Experience for General Continual Learning: a Strong, Simple Baseline

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Setup

• Use ./utils/main.py to run experiments.
• Use argument --load_best_args to use the best hyperparameters from the paper.
• New models can be added to the models/ folder.
• New datasets can be added to the datasets/ folder.

Models

• eXtended-DER (X-DER)
• Dark Experience Replay (DER)
• Dark Experience Replay++ (DER++)
• Learning a Unified Classifier Incrementally via Rebalancing (LUCIR)
• Greedy Sampler and Dumb Learner (GDumb)
• Bias Correction (BiC)
• Regular Polytope Classifier (RPC)
• Gradient Episodic Memory (GEM)
• A-GEM
• A-GEM with Reservoir (A-GEM-R)
• Experience Replay (ER)
• Meta-Experience Replay (MER)
• Function Distance Regularization (FDR)
• Greedy gradient-based Sample Selection (GSS)
• Hindsight Anchor Learning (HAL)
• Incremental Classifier and Representation Learning (iCaRL)
• online Elastic Weight Consolidation (oEWC)
• Synaptic Intelligence (SI)
• Learning without Forgetting (LwF)
• Progressive Neural Networks (PNN)

Datasets

• Sequential MNIST (Class-Il / Task-IL)
• Sequential CIFAR-10 (Class-Il / Task-IL)
• Sequential Tiny ImageNet (Class-Il / Task-IL)
• Sequential CIFAR-100 (Class-Il / Task-IL)
• Permuted MNIST (Domain-IL)
• Rotated MNIST (Domain-IL)
• MNIST-360 (General Continual Learning)

Citing these works

@article{boschini2022class,
  title={Class-Incremental Continual Learning into the eXtended DER-verse},
  author={Boschini, Matteo and Bonicelli, Lorenzo and Buzzega, Pietro and Porrello, Angelo and Calderara, Simone},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2022},
  publisher={IEEE}
}

@inproceedings{buzzega2020dark,
 author = {Buzzega, Pietro and Boschini, Matteo and Porrello, Angelo and Abati, Davide and Calderara, Simone},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},
 pages = {15920--15930},
 publisher = {Curran Associates, Inc.},
 title = {Dark Experience for General Continual Learning: a Strong, Simple Baseline},
 volume = {33},
 year = {2020}
}

Awesome Papers using Mammoth

Our Papers

• Dark Experience for General Continual Learning: a Strong, Simple Baseline (NeurIPS 2020) [paper]
• Rethinking Experience Replay: a Bag of Tricks for Continual Learning (ICPR 2020) [paper] [code]
• Class-Incremental Continual Learning into the eXtended DER-verse (TPAMI 2022) [paper]
• Effects of Auxiliary Knowledge on Continual Learning (ICPR 2022) [paper]
• Transfer without Forgetting (ECCV 2022) [paper][code]
• Continual semi-supervised learning through contrastive interpolation consistency (PRL 2022) [paper][code]
• On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning (NeurIPS 2022) [paper] [code]

Other Awesome CL works using Mammoth

• New Insights on Reducing Abrupt Representation Change in Online Continual Learning (ICLR2022) [paper] [code]
• Learning fast, learning slow: A general continual learning method based on complementary learning system (ICLR2022) [paper] [code]
• Self-supervised models are continual learners (CVPR2022) [paper] [code]
• Representational continuity for unsupervised continual learning (ICLR2022) [paper] [code]
• Continual Learning by Modeling Intra-Class Variation (TMLR 2023) [paper] [code]
• Consistency is the key to further Mitigating Catastrophic Forgetting in Continual Learning (CoLLAs2022) [paper] [code]
• Continual Normalization: Rethinking Batch Normalization for Online Continual Learning (ICLR2022) [paper] [code]
• NISPA: Neuro-Inspired Stability-Plasticity Adaptation for Continual Learning in Sparse Networks (ICML2022) [paper]
• Learning from Students: Online Contrastive Distillation Network for General Continual Learning (IJCAI2022) [paper] [code]

Update Roadmap

In the near future, we plan to incorporate the following improvements into this master repository:

• ER+Tricks (Rethinking Experience Replay: a Bag of Tricks for Continual Learning)
• TwF & Pretraining Baselines (Transfer without Forgetting)
• CCIC & CSSL Baselines (Continual semi-supervised learning through contrastive interpolation consistency)
• LiDER (On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning)
• Additional X-DER datasets (Class-Incremental Continual Learning into the eXtended DER-verse)

Pull requests welcome! Get in touch

Previous versions

If you're interested in a version of this repo that only includes the code for Dark Experience for General Continual Learning: a Strong, Simple Baseline, please use our neurips2020 tag.