This is the official PyTorch Implementation of "SoTTA: Robust Test-Time Adaptation on Noisy Data Streams (NeurIPS '23)" by Taesik Gong*, Yewon Kim*, Taeckyung Lee*, Sorn Chottananurak, and Sung-Ju Lee (* Equal contribution).
Home Page: https://nmsl.kaist.ac.kr/projects/sotta
License: MIT License
Please visit https://taeckyung.github.io for more information.
Hi, author,
Thanks for your code and paper.
I have a few questions about the evaluation protocol. Let's take CIFAR-10-C as an example, for the ''Near'' scenario, the online data stream should consists of the original target samples (i.e., Benign) of CIFAR-10-C and the CIFAR-100 test set (if I understand right).
So the model need to classify only the Benign samples or it has to both classify Benign samples and identify samples from CIFAR-100 test set?
And accordingly, the dimension of the classifier is unchanged or the the classifier is expanded to ''C+1''-dim? C for number of categories and 1 for anomaly detection.
Hopefully you can explain more about the new setting. Thx.
Hi, This is a great job! When I downloaded the dataset of CIFAR-10-C, it showed that "HTTP request sent, awaiting response... 403 FORBIDDEN", could you please send the datasets to me by email? Thanks.
Nice work! And by the way, could you please provide the source pretrained model for Imagenet and Mnist? Thank you!
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