📃 Enhancing Few-Shot Class-Incremental Learning through Frozen Feature Augmentation

This is the Pytorch Implementation for the paper:

Abstract: Few-shot class incremental learning (FSCIL) poses a significant challenge, as it not only requires mitigating catas- trophic forgetting but also addressing overfitting. Previous studies have primarily focused on alleviating these issues through repre- sentation learning, with the goal of enhancing the separation between base classes and providing adequate representation space for incremental classes. As pre-trained models become increasingly prevalent in continual learning, exploring frozen feature augmentation for FSCIL becomes extremely necessary. This work proposes an ingenious brightness-based frozen feature augmentation method for FSCIL. We validate its effectiveness on both regular and multi-modal continual learning datasets, including CIFAR 100, UESTC-MMEA-CL, and ARIC. Not only that, we analyze the decision attribution stability change afforded by such a feature augmentation, demonstrating that it does not compromise interpretability.

🔧 Dependencies and Installation

• PyTorch >= version 1.13.1
• tqdm
• matplotlib
• pandas
• scikit-learn
• numpy
• prettytable
• timm

Installation

First, you can clone this repo using the command:

git clone https://github.com/learninginvision/FAOrCo-ViT

Then, you can create a virtual environment using conda, as follows:

conda env create -f environment.yaml
conda activate FAOrCo-ViT

💾 Data preparation

We follow FSCIL setting and use the same data index_list for training splits across incremental sessions. The datasets are made readily available by the authors of CEC in their github repository here. Follow their provided instructions to download and unzip. We assume in our code that the datasets are present inside a datasets folder on the same directory level as the cloned repository. If this is not the case then you will need to overwrite the correct path in the shell script.

💻 Training

Run the following command to train the model sequentially:

bash ./scripts/train_cifar100.sh

After training, you can get model checkpoints in the folder ./checkpoint_wd0.0/cifar100/orco_vit.

📊 Evaluation

After completing the training, update the "-output_dir" parameter in ./test_scripts/test_cifar100.sh with the directory where your model is saved. Then, run the following command:

bash ./test_scripts/test_cifar100.sh

The result will be saved in the the directory where your model is saved.

📈 Results

Results compared with prior work.

📜 Acknowledgments

Our project references the codes in the following repositories.

• CEC
• OrCo

We thank the authors for releasing their code.