This repo contains code for our paper: Learning Semi-supervised Gaussian Mixture Models for Generalized Category Discovery
pip install -r requirements.txt
Set paths to datasets, pre-trained models and desired log directories in config.py
.
Also set the experiment paths in bash_scripts/run.sh
.
We use fine-grained benchmarks in this paper, including:
We also use generic object recognition datasets, including:
- CIFAR-10/100 and ImageNet
Please follow this repo or this repo to set up the data.
Train representation:
bash bash_scripts/run.sh
If you use this code in your research, please consider citing our paper:
@InProceedings{Zhao_2023_ICCV,
author = {Zhao, Bingchen and Wen, Xin and Han, Kai},
title = {Learning Semi-supervised Gaussian Mixture Models for Generalized Category Discovery},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2023},
pages = {16623-16633}
}
The codebase is largely built on this repo: https://github.com/sgvaze/generalized-category-discovery.