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Found 7 papers, 2 papers with code
Explaining YOLO: Leveraging Grad-CAM to Explain Object Detections
We investigate the problem of explainability for visual object detectors.
Explaining machine learning models for age classification in human gait analysis
Machine learning (ML) models have proven effective in classifying gait analysis data, e. g., binary classification of young vs. older adults.
Explaining automated gender classification of human gait
State-of-the-art machine learning (ML) models are highly effective in classifying gait analysis data, however, they lack in providing explanations for their predictions.
Multimodal Detection of Information Disorder from Social Media
Social media is accompanied by an increasing proportion of content that provides fake information or misleading content, known as information disorder.
Bounded logit attention: Learning to explain image classifiers
Explainable artificial intelligence is the attempt to elucidate the workings of systems too complex to be directly accessible to human cognition through suitable side-information referred to as "explanations".
On the Explanation of Machine Learning Predictions in Clinical Gait Analysis
Machine learning (ML) is increasingly used to support decision-making in the healthcare sector.
Automatic Classification of Functional Gait Disorders
The aim of the study is twofold: (1) to investigate the suitability of stateof-the-art GRF parameterization techniques (representations) for the discrimination of functional gait disorders; and (2) to provide a first performance baseline for the automated classification of functional gait disorders for a large-scale dataset.
