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Found 8 papers, 5 papers with code
Identification of Fine-grained Systematic Errors via Controlled Scene Generation
Our approach, BEV2EGO, allows for a realistic generation of the complete scene with road-contingent control that maps 2D bird's-eye view (BEV) scene configurations to a first-person view (EGO).
Generating Realistic Counterfactuals for Retinal Fundus and OCT Images using Diffusion Models
Counterfactual reasoning is often used in clinical settings to explain decisions or weigh alternatives.
Identifying Systematic Errors in Object Detectors with the SCROD Pipeline
Moreover, our framework introduces an evaluation setting that can serve as a benchmark for similar pipelines.
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.
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.
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.
