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Found 4 papers, 3 papers with code
SAFE: Saliency-Aware Counterfactual Explanations for DNN-based Automated Driving Systems
A CF explainer identifies the minimum modifications in the input that would alter the model's output to its complement.
Heterogeneous Graph Learning for Acoustic Event Classification
Heterogeneous graphs provide a compact, efficient, and scalable way to model data involving multiple disparate modalities.
Visually-aware Acoustic Event Detection using Heterogeneous Graphs
In contrast, we employ heterogeneous graphs to explicitly capture the spatial and temporal relationships between the modalities and represent detailed information about the underlying signal.
Self-supervised Graphs for Audio Representation Learning with Limited Labeled Data
Large scale databases with high-quality manual annotations are scarce in audio domain.
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