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**NAPA-VQ: Neighborhood-Aware Prototype Augmentation with Vector Quantization for Continual Learning**

We propose NAPA-VQ: Neighborhood Aware Prototype Augmentation with Vector Quantization, a framework that reduces catastrophic forgetting in **Non-Exemplar based Class Incremental Learning.**

[Paper](https://openaccess.thecvf.com/content/ICCV2023/html/Malepathirana_NAPA-VQ_Neighborhood-Aware_Prototype_Augmentation_with_Vector_Quantization_for_Continual_Learning_ICCV_2023_paper.html)

#### Requirements
Python 3.7.4

PyTorch: 1.9.0

#### Datasets
Create the "dataset" directory under NAPA-VQ and download the following datasets into the created directory.

1.CIFAR-100
    
    2.Tiny-ImageNet
    
    3.ImageNet-Subset

#### Training

CIFAR-100

``` 
# 5 Tasks
python main_cifar.py --fg_nc 50 --task_num 5 --custom_name "cifar_100-5" --base_model "cifar_100-5" --epochs 100 --shuffle > cifar-5.txt
# 10 Tasks
python main_cifar.py --fg_nc 50 --task_num 10 --custom_name "cifar_100-10" --base_model "cifar_100-10" --epochs 100 --shuffle > cifar-10.txt
# 20 Tasks
python main_cifar.py --fg_nc 40 --task_num 20 --custom_name "cifar_100-20" --base_model "cifar_100-20" --epochs 100 --shuffle > cifar-20.txt

```

Tiny-ImageNet
```
# 5 Tasks
python main_tiny.py --fg_nc 100 --task_num 5 --base_model "tiny-5" --custom_name "tiny-5" --shuffle --epochs 50 > tiny-5.txt
# 10 Tasks
python main_tiny.py --fg_nc 100 --task_num 10 --base_model "tiny-10" --custom_name "tiny-10" --shuffle --epochs 50 > tiny-10.txt
# 20 Tasks
python main_tiny.py --fg_nc 100 --task_num 20 --base_model "tiny-20" --custom_name "tiny-20" --shuffle --epochs 50 > tiny-20.txt
```

ImageNet-Subset

```
# 5 Tasks
python main_imagenet.py --custom_name "imagenet-5" --base_model "imagenet-5" --fg_nc 50 --task_num 5 --shuffle > imagenet-5.txt
# 10 Tasks
python main_imagenet.py --custom_name "imagenet-10" --base_model "imagenet-10" --fg_nc 50 --task_num 10 --shuffle > imagenet-10.txt
# 20 Tasks
python main_imagenet.py --custom_name "imagenet-20" --base_model "imagenet-20" --fg_nc 40 --task_num 20 --shuffle > imagenet-20.txt

```

### Citation
<pre>
    <code>
    @InProceedings{Malepathirana_2023_ICCV,
        author    = {Malepathirana, Tamasha and Senanayake, Damith and Halgamuge, Saman},
        title     = {NAPA-VQ: Neighborhood-Aware Prototype Augmentation with Vector Quantization for Continual Learning},
        booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
        month     = {October},
        year      = {2023},
        pages     = {11674-11684}
    }
    </code>
</pre>

#### References

We thank the authors of the following repositories for their excellent codebase providing reusable functions.

https://github.com/Impression2805/CVPR21_PASS

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