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Found 10 papers, 6 papers with code
Analyzing and Explaining Image Classifiers via Diffusion Guidance
While deep learning has led to huge progress in complex image classification tasks like ImageNet, unexpected failure modes, e. g. via spurious features, call into question how reliably these classifiers work in the wild.
Spurious Features Everywhere - Large-Scale Detection of Harmful Spurious Features in ImageNet
In contrast, we work with ImageNet and validate our results by showing that presence of the harmful spurious feature of a class alone is sufficient to trigger the prediction of that class.
Spurious Features Everywhere -- Large-Scale Detection of Harmful Spurious Features in ImageNet
In contrast, we work with ImageNet and validate our results by showing that presence of the harmful spurious feature of a class alone is sufficient to trigger the prediction of that class.
Diffusion Visual Counterfactual Explanations
Two modifications to the diffusion process are key for our DVCEs: first, an adaptive parameterization, whose hyperparameters generalize across images and models, together with distance regularization and late start of the diffusion process, allow us to generate images with minimal semantic changes to the original ones but different classification.
Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities
Moreover, we show that the confidence loss which is used by Outlier Exposure has an implicit scoring function which differs in a non-trivial fashion from the theoretically optimal scoring function in the case where training and test out-distribution are the same, which again is similar to the one used when training an Energy-Based OOD detector or when adding a background class.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Sparse Visual Counterfactual Explanations in Image Space
Visual counterfactual explanations (VCEs) in image space are an important tool to understand decisions of image classifiers as they show under which changes of the image the decision of the classifier would change.
Revisiting Out-of-Distribution Detection: A Simple Baseline is Surprisingly Effective
When trained in a shared fashion with a standard classifier, this binary discriminator reaches an OOD detection performance similar to that of Outlier Exposure.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
The Needle in the haystack: Out-distribution aware Self-training in an Open-World Setting
Traditional semi-supervised learning (SSL) has focused on the closed world assumption where all unlabeled samples are task-related.
Out-distribution aware Self-training in an Open World Setting
The goal of this paper is to leverage unlabeled data in an open world setting to further improve prediction performance.
Adversarial Robustness on In- and Out-Distribution Improves Explainability
Neural networks have led to major improvements in image classification but suffer from being non-robust to adversarial changes, unreliable uncertainty estimates on out-distribution samples and their inscrutable black-box decisions.
