Explainability

Quantitative Metrics for Evaluating Explanations of Video DeepFake Detectors
BMVC 2022 - Quantitative metrics for evaluating explanations of video DeepFake detectors, in collaboration with the Huawei Ireland Research Center.
Federico Baldassarre, Quentin Debard, Gonzalo Fiz Pontiveros, Tri Kurniawan Wijaya
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Quantitative Metrics for Evaluating Explanations of Video DeepFake Detectors
Explanation-based Weakly-supervised Learning of Visual Relations with Graph Networks
ECCV 2020 - A novel weakly-supervised method for visual relationship detection that relies on minimal image-level predicate labels, graph networks, and attribution-based explanations.
Federico Baldassarre, Kevin Smith, Josephine Sullivan, Hossein Azizpour
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Explanation-based Weakly-supervised Learning of Visual Relations with Graph Networks
Explainability Techniques for Graph Convolutional Networks
ICML Workshop 2019 - How do Sensitivity Analysis, Guided Backpropagation, and Layer-Wise Relevance Propagation behave when applied to Graph Neural Networks?
Federico Baldassarre, Hossein Azizpour
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Explainability Techniques for Graph Convolutional Networks