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Found 9 papers, 2 papers with code
Layerwise Early Stopping for Test Time Adaptation
In this paper, we propose Layerwise EArly STopping (LEAST) for TTA to address this problem.
Generalizing across Temporal Domains with Koopman Operators
By employing Koopman Operators, we effectively address the time-evolving distributions encountered in TDG using the principles of Koopman theory, where measurement functions are sought to establish linear transition relations between evolving domains.
Image-to-Image Translation with Low Resolution Conditioning
This differs from previous methods that focus on translating a given image style into a target content, our translation approach being able to simultaneously imitate the style and merge the structural information of the LR target.
Ranked #5 on
Image-to-Image Translation
on CelebA-HQ
Self-supervised Robust Object Detectors from Partially Labelled Datasets
With the goal of training \emph{one integrated robust object detector with high generalization performance}, we propose a training framework to overcome missing-label challenge of the merged datasets.
Toward Adversarial Robustness by Diversity in an Ensemble of Specialized Deep Neural Networks
Using MNIST and CIFAR-10, we empirically verify the ability of our ensemble to detect a large portion of well-known black-box adversarial examples, which leads to a significant reduction in the risk rate of adversaries, at the expense of a small increase in the risk rate of clean samples.
A Novel Unsupervised Post-Processing Calibration Method for DNNS with Robustness to Domain Shift
The uncertainty estimation is critical in real-world decision making applications, especially when distributional shift between the training and test data are prevalent.
Toward Metrics for Differentiating Out-of-Distribution Sets
We empirically verify that the most protective OOD sets -- selected according to our metrics -- lead to A-CNNs with significantly lower generalization errors than the A-CNNs trained on the least protective ones.
Unsupervised Temperature Scaling: Robust Post-processing Calibration for Domain Shift
The uncertainty estimation is critical in real-world decision making applications, especially when distributional shift between the training and test data are prevalent.
Controlling Over-generalization and its Effect on Adversarial Examples Generation and Detection
As an appropriate training set for the extra class, we introduce two resources that are computationally efficient to obtain: a representative natural out-distribution set and interpolated in-distribution samples.
