This is the official Implementation of the paper https://openaccess.thecvf.com/content/WACV2024/papers/Banerjee_AMEND_Adaptive_Margin_and_Expanded_Neighborhood_for_Efficient_Generalized_Category_WACV_2024_paper.pdf
Generalized Category Discovery aims to discover and cluster images from previously unseen classes, in addition to classifying images from seen classes correctly. In this work, we propose a simple, yet effective framework for this task, which not only performs on-par or better with the current approaches but is also significantly more efficient in terms of computational requirements. Our first contribution is to use expanded neighborhood information in contrastive learning to generate robust and generalizable features. To generate more discriminative feature representations, especially for fine-grained datasets and confusing classes, we propose a class-wise adaptive margin regularizer that aims at increasing the angular separation among the prototypes of all classes. Extensive experiments on three generic as well as four fine-grained benchmark datasets show the usefulness of the proposed Adaptive Margin and Expanded Neighborhood (AMEND) framework.
pip install -r requirements.txt
Set paths to datasets and desired log directories in config.py
We use fine-grained benchmarks in this paper, including:
We also use generic object recognition datasets, including:
- CIFAR-10/100 and ImageNet
Train the model:
bash scripts/run_${DATASET_NAME}.sh
Then check the results starting with Metrics with best model on test set: in the logs.
This means the model is picked according to its performance on the test set, and then evaluated on the unlabelled instances of the train set.
The codebase is largely built on this repo: https://github.com/CVMI-Lab/SimGCD