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Found 7 papers, 1 papers with code
Anomaly-Aware Semantic Segmentation via Style-Aligned OoD Augmentation
Within the context of autonomous driving, encountering unknown objects becomes inevitable during deployment in the open world.
Identification of Systematic Errors of Image Classifiers on Rare Subgroups
Despite excellent average-case performance of many image classifiers, their performance can substantially deteriorate on semantically coherent subgroups of the data that were under-represented in the training data.
Test-Time Adaptation to Distribution Shift by Confidence Maximization and Input Transformation
This paper focuses on the fully test-time adaptation setting, where only unlabeled data from the target distribution is required.
Test-Time Adaptation to Distribution Shifts by Confidence Maximization and Input Transformation
This paper focuses on the fully test-time adaptation setting, where only unlabeled data from the target distribution is required.
Does enhanced shape bias improve neural network robustness to common corruptions?
We conclude that the data augmentation caused by style-variation accounts for the improved corruption robustness and increased shape bias is only a byproduct.
Meta Adversarial Training against Universal Patches
However, tailoring adversarial training to universal patches is computationally expensive since the optimal universal patch depends on the model weights which change during training.
Increasing the Robustness of Semantic Segmentation Models with Painting-by-Numbers
With respect to our 16 different types of image corruptions and 5 different network backbones, we are in 74% better than training with clean data.
