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https://github.com/Mattdl/ContinualPrototypeEvolution

Codebase for Continual Prototype Evolution (CoPE) to attain perpetually representative prototypes for online and non-stationary datastreams. Includes implementation of the Pseudo-Prototypical Proxy (PPP) loss.
https://github.com/Mattdl/ContinualPrototypeEvolution

computer-vision continual-learning datastreaming deep-learning online-learning prototypes representation-learning

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Codebase for Continual Prototype Evolution (CoPE) to attain perpetually representative prototypes for online and non-stationary datastreams. Includes implementation of the Pseudo-Prototypical Proxy (PPP) loss.

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README 

        
# Continual Prototype Evolution (CoPE)



Continual Prototype Evolution (CoPE) establishes online adaptation of class-representative prototypes in non-stationary data streams, exploiting latent space representations in the novel PPP-loss to enhance the state-of-the-art in continual learning.



This codebase contains the original PyTorch implementation of CoPE, along with the Split-MNIST, Split-CIFAR10, Split-CIFAR100 benchmarks.

The benchmarks have both a balanced and highly imbalanced variant, resembling more real-life settings.

Included baselines outperformed in these settings are: CoPE-CrossEntropy, GEM, iCaRL, GSS, reservoir sampling, finetuning, online iid, offline iid.



- This work is accepted at the International Conference on Computer Vision (ICCV) 2021.

- Resources: **[Open-Access paper @ICCV 2021](https://openaccess.thecvf.com/content/ICCV2021/html/De_Lange_Continual_Prototype_Evolution_Learning_Online_From_Non-Stationary_Data_Streams_ICCV_2021_paper.html)** | **[Supplemental Materials](https://openaccess.thecvf.com/content/ICCV2021/supplemental/De_Lange_Continual_Prototype_Evolution_ICCV_2021_supplemental.pdf)**






**Keywords**: continual learning, prototypical learning, online learning, incremental learning, deep learning, representation learning, catastrophic forgetting, concept drift



## Results

Main scripts [main_MNIST.sh](main_MNIST.sh), [main_CIFAR10.sh](main_CIFAR10.sh), [main_CIFAR100.sh](main_CIFAR100.sh)

contain fully automatic pipeline (auto datapreparation), with hyperparameter configs for all of the experiments in the main paper.



The **balanced** setups contain: 

- Split-MNIST, Split-CIFAR10, Split-CIFAR100 and 

- lower capacity benchmarks Split-MNIST-mini and Split-CIFAR10-mini.



The **imbalanced** setups contain (averaged over 5 different choices of dominant task):

- Imbalanced Split-MNIST: 1 task 2k samples, others 0.2k (5 tasks)

- Imbalanced Split-CIFAR10: 1 task 4k samples, others 0.4k (5 tasks)

- Imbalanced Split-CIFAR100: 1 task 2k samples, others 1k (20 tasks)



## Requirements

- Python 3.7

- Pytorch 1.5 ([instructions](https://pytorch.org/get-started/previous-versions/#v150))

- To install dependencies:

    - Use [environment.yml](environment.yml) to create anaconda environment:

        

            conda env create -f environment.yml         # Env named 'cope'

            conda activate cope

    - Or manually, as in:

    

            # Create and activate environment

            conda create -n  python=3.7

            conda activate 



            # Pytorch (e.g. for CUDA 10.2)

            conda install pytorch==1.5.0 torchvision==0.6.0 cudatoolkit=10.2 -c pytorch



            # Optional

            conda install -c conda-forge matplotlib=3.1.3       # T-SNE plots

            conda install -c conda-forge scikit-learn=0.22.1

            conda install -c omnia quadprog                     # GEM baseline



## Reproducing paper results

This final code-base is validated to produce similar results to the original results reported in the paper.

- To avoid issues, use the *exact* dependency requirements defined above.

- Original implementation doesn't re-normalize prototypes after momentum-update. Doing this slightly decreases average accuracy.

- Final code-base results after cleanup are checked. Avg. accuracy balanced benchmarks: 94.11+-0.76 (MNIST 5 seeds), 49.61+-3.44 (CIFAR10 5 seeds), 20.51 (CIFAR100 1 seed). 

Report an issue or contact me if you have troubles reproducing these.



## Online Data incremental learning

Although the data streams are divided into tasks to compare with task and class-incremental learning alorithms (iCaRL, GEM),

in CoPE the continual learner is unaware of tasks or task transitions.

This means CoPE can learn from any labeled data stream, without the bias of hand-designed task boundaries within the stream.



## Learner-evaluator framework

The learner-evaluator framework defined in the [paper](https://arxiv.org/pdf/2009.00919.pdf), explicitly models all the 

requirements of the continual learning system. 



We define the **learner** here for CoPE:

- The horizon = the currently observed batch (online processing)

- The operational memory = replay memory + prototypical memory



With the **evaluator**:

- Periodicity (rho) = evaluating on task transitions

- Eval distribution = static class distributions, evaluate on observed classes in learner



## Credits

- Consider citing our work upon using this repo.



        @InProceedings{De_Lange_2021_ICCV,

            author    = {De Lange, Matthias and Tuytelaars, Tinne},

            title     = {Continual Prototype Evolution: Learning Online From Non-Stationary Data Streams},

            booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},

            month     = {October},year = {2021}, pages = {8250-8259}

        }

- CoPE has been made available in the [Avalanche framework](https://github.com/ContinualAI/avalanche/) (free to use under MIT license)!

- Thanks to the following repositories:

    - https://github.com/facebookresearch/GradientEpisodicMemory

    - https://github.com/rahafaljundi/Gradient-based-Sample-Selection

    - https://github.com/Mattdl/CLsurvey



This source code is released under a Attribution-NonCommercial 4.0 International license, find out more about it in the [LICENSE file](LICENSE).