Image Clustering Using an Augmented Generative Adversarial Network and Information Maximization
Introduction
**This is an implementation code written in Python (version 3.6.9) of a manuscript paper **
Performance
The reported performance of our proposed model is based on custom architecture.
We train our unsupervised proposed method for 5 independent runs on training and testing set and we report the results accordingly.
Best and Average Performance
Below table reports the best and average recorded performance from our model.
| Dataset | Best Acc | Aver Acc |
|---|---|---|
| MNIST | 99.02% | 98.85% (±0.14%) |
| CIFAR-10 | 70.04% | 69.22% (±0.83%) |
| CIFAR-100/20 | 32.44% | 30.88% (±0.14%) |
| STL10 | 58.65 % | 74.7% (±1.81%) |
Usage
All hyper-parameters of the reported accuracy are stored in 'models.py'. To run the training code.
MNIST
python train.py --dataset mnist --store_path ./models/mnist/mnist.ckpt
CIFAR-10
To run the training code.
python train.py --dataset c10 --store_path ./models/c10/c10.ckpt
CIFAR-100/20
To run the training code.
python train.py --dataset c100 --store_path ./models/c100/c100.ckpt
STL10
To run the training code.
python train.py --dataset stl10 --store_path ./models/stl10/stl10.ckpt
The evaluation of the model.
Example evaluation on MNIST, CIFAR10/100-20 & STL10:
Through the argument '--store_path', the full path of the stored model is parsed.
python test.py --dataset mnist --store_path ./models/mnist/mnist.ckpt
python test.py --dataset c10 --store_path ./models/c10/c10.ckpt
python test.py --dataset c100 --store_path ./models/c100/c100.ckpt
python test.py --dataset stl10 --store_path ./models/stl10/stl10.ckpt
Notes
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The classifier head is trained and evaluated only for labelled set on STL10 dataset. The unlabelled part of STL10 is used only to train the GAN model.
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All tests have been performed in Cuda version 10.1.
Citation
@ARTICLE{9451540,
author={Ntelemis, Foivos and Jin, Yaochu and Thomas, Spencer A.},
journal={IEEE Transactions on Neural Networks and Learning Systems},
title={Image Clustering Using an Augmented Generative Adversarial Network and Information Maximization},
year={2021},
volume={},
number={},
pages={1-14},
doi={10.1109/TNNLS.2021.3085125}}