FSS

Fully Self-Supervised Masked Autoencoders for Out-of-domain Few-Shot Learning (FSS), a novel technique that adapts a vision transformer (ViT) to new domains through application of an on-line self-supervised finetuning session.

Experiments

Test FSS on a range of out-of-domain datasets.

Requirements

Install the requirements necessary for using FSS. A virtual environment (or similar) is recommended.

pip install -r requirements.txt

Datasets

Datasets used in this work can be obtained at the following links:

• BCCD WBC
• ISIC, EuroSAT

After downloading all datasets, you should extract/place each respective dataset's folder in the same directory.

Pretrained Weights

You can download MAE pretrained weights from Meta's MAE Implementation

To use the pretrained weights, create a fit_models folder at the root of this repo and place all pretrained weights in it.

Running Experiments

Run the mae_test.sh. To adjust parameters for a testing run, edit the number of shots, the finetuning iterations, image size, model type, model weights path, and the path to the root data directory (where you placed the downloaded datasets earlier).

To see a full list of available options, run the following command to see the help dialogue:

python3 mae_test.py --help

In-Domain and Out-of-Domain Results