Official repository of the CVPR 2024 paper "NC-TTT: A Noise Constrastive Approach for Test-Time Training", by David Osowiechi, Gustavo A. Vargas Hakim, Mehrdad Noori, Milad Cheraghalikhani, Ali Bahri, Moslem Yazdanpanah, Ismail Ben Ayed, and Christian Desrosiers. The whole article can be found here. This work was greatly inspired by the code in ClusT3.
We propose a novel unsupervised TTT technique based on the discrimination of noisy feature maps. By learning to classify noisy views of projected feature maps, and then adapting the model accordingly on new domains, classification performance can be recovered by an important margin.
The experiments utilize the CIFAR-10 training split as the source dataset. It can be downloaded from
CIFAR-10, or can also be done using torchvision
datasets: train_data = torchvision.datasets.CIFAR10(root='Your/Path/To/Data', train=True, download=True).
The same line of code can be used to load the data if it is already downloaded, just by changing the
argument download to False.
At test-time, we use CIFAR-10-C and CIFAR-10-new. The first one can be downloaded from CIFAR-10-C. For the second one, please download the files
cifar10.1_v6_data.npy and cifar10.1_v6_labels.npy from CIFAR-10-new.
All the data should be placed in a common folder from which they can be loaded, e.g., /datasets/.
The training works the same way on CIFAR-100 dataset and it can be downloaded from CIFAR-100. At test-time, we use CIFAR-100-C which can be downloaded from CIFAR-100-C.
If you found this repository, or its related paper useful for your research, you can cite this work as:
@inproceedings{NCTTT2024,
title={NC-TTT: A Noise Constrastive Approach for Test-Time Training},
author={David Osowiechi and Gustavo A. Vargas Hakim and Mehrdad Noori and Milad Cheraghalikhani and Ali Bahri and Moslem Yazdanpanah and Ismail Ben Ayed and Christian Desrosiers},
booktitle={***},
pages={},
month={June},
year={2024}
}