CLearning

KO | EN

gitlite — search

#javascript #python #react #golang #go #android #linux #macos #cli #awesome-list #vue #security #mcp #ios

(★ 14)

CLearning is a general continual learning framework

CLearning Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

## CLearning is a general continual learning framework.

### 1. Features
- Supported incremental learning (IL) scenarios:
  
    Task-IL, Domain-IL, and Class-IL
  
- Supported datasets:
  
    MNIST, PMNIST, RMNIST, FIVE, SVHN, CIFAR10, CIFAR100, SUPER, TinyImageNet, miniImageNet, DTD, ImageNet100, CUB, ImageNet1000

- Supported training modes:
  
    Single GPU, Distributed Parallel (DP)
  
- Support flexibly specify model:
  
    model specified in '*.yaml' > default model

### 2. Methods
The current repository implements the following continual learning methods:

- Baseline
    - FineTune
- Regularization
    - EWC: [Overcoming catastrophic forgetting in neural networks](https://www.pnas.org/doi/abs/10.1073/pnas.1611835114)
    - LwF: [Learning without forgetting](https://ieeexplore.ieee.org/abstract/document/8107520/)
    - GPM: [Gradient projection memory for continual learning](https://arxiv.org/abs/2103.09762)
- Replay
    - AFC: [Class-incremental learning by knowledge distillation with adaptive feature consolidation](http://openaccess.thecvf.com/content/CVPR2022/html/Kang_Class-Incremental_Learning_by_Knowledge_Distillation_With_Adaptive_Feature_Consolidation_CVPR_2022_paper.html)
    - ANCL: [Achieving a better stability-plasticity trade-off via auxiliary networks in continual learning](http://openaccess.thecvf.com/content/CVPR2023/html/Kim_Achieving_a_Better_Stability-Plasticity_Trade-Off_via_Auxiliary_Networks_in_Continual_CVPR_2023_paper.html)
    - CSCCT: [Class-incremental learning with cross-space clustering and controlled transfer](https://link.springer.com/chapter/10.1007/978-3-031-19812-0_7)
    - iCaRL: [icarl: Incremental classifier and representation learning](http://openaccess.thecvf.com/content_cvpr_2017/html/Rebuffi_iCaRL_Incremental_Classifier_CVPR_2017_paper.html)
    - LUCIR: [Learning a unified classifier incrementally via rebalancing](http://openaccess.thecvf.com/content_CVPR_2019/html/Hou_Learning_a_Unified_Classifier_Incrementally_via_Rebalancing_CVPR_2019_paper.html)
    - MTD: [Class Incremental Learning with Multi-Teacher Distillation](https://openaccess.thecvf.com/content/CVPR2024/html/Wen_Class_Incremental_Learning_with_Multi-Teacher_Distillation_CVPR_2024_paper.html)
    - OPC: [Optimizing mode connectivity for class incremental learning](https://proceedings.mlr.press/v202/wen23b.html)
    - PODNet: [Podnet: Pooled outputs distillation for small-tasks incremental learning](https://link.springer.com/chapter/10.1007/978-3-030-58565-5_6)
    - SSIL: [Ss-il: Separated softmax for incremental learning](http://openaccess.thecvf.com/content/ICCV2021/html/Ahn_SS-IL_Separated_Softmax_for_Incremental_Learning_ICCV_2021_paper.html)
- Structure
    - AANet: [Adaptive aggregation networks for class-incremental learning](http://openaccess.thecvf.com/content/CVPR2021/html/Liu_Adaptive_Aggregation_Networks_for_Class-Incremental_Learning_CVPR_2021_paper.html)

### 3. Environment

#### 3.1. Code Environment

- python==3.9
- pytorch==1.11.0
- continuum==1.2.4
- ...

The required packages are listed in files 'config/requirements.txt' and 'config/env.yml'. We recommend using Anaconda to manage packages, such as:
```
conda create -n torch1.11.0 python=3.9
conda activate torch1.11.0
conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch
cd config
pip install -r requirements.txt
```

#### 3.2. Dataset

Folder Structures
```
├── data
│   ├── cifar-100-python
│   ├── ImageNet
│   ├── ImageNet100
```
The CIFAR-100 dataset can be automatically downloaded by running an arbitrary script of CIFAR-100 experiments. The [ImageNet](https://image-net.org) dataset must be pre-downloaded. The ImageNet-100 dataset can be generated by the script 'tool/gen_imagenet100.py'.

### 4. Command
```` python 
python main.py --cfg config/baseline/finetune/finetune_cifar100.yaml --device 0 --note test
````

### 5. Acknowledgements

We appreciate the following GitHub repos a lot for their valuable code base:
- https://github.com/arthurdouillard/incremental_learning.pytorch
- https://github.com/yaoyao-liu/POD-AANets

### 6. Citation
We hope that our research can help you and promote the development of continual learning.
If you find this work useful, please consider citing the corresponding paper:

```
@inproceedings{wen2024class,
  title={Class Incremental Learning with Multi-Teacher Distillation},
  author={Wen, Haitao and Pan, Lili and Dai, Yu and Qiu, Heqian and Wang, Lanxiao and Wu, Qingbo and Li, Hongliang},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={28443--28452},
  year={2024}
}

@InProceedings{pmlr-v202-wen23b,
  title = {Optimizing Mode Connectivity for Class Incremental Learning},
  author = {Wen, Haitao and Cheng, Haoyang and Qiu, Heqian and Wang, Lanxiao and Pan, Lili and Li, Hongliang},
  booktitle = {Proceedings of the 40th International Conference on Machine Learning},
  pages = {36940--36957},
  year = {2023},
  volume = {202},
  publisher = {PMLR}
}
```

CLearning

CLearning Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

CLearning

CLearning Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README