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insikt - Deep Learning Interpretability
Searching for Internal Symbols Underlying Deep Learning Decisions
Deep neural networks (DNNs) may develop abstract internal representations, termed "symbols," which can be extracted and used to understand, improve, and safeguard DNN decision-making.
Disambiguating Multiple Class Labels in Deep Learning Image Recognition: A Counterfactual Proof Framework
This research paper proposes a novel framework and method for disambiguating multiple class label predictions in deep learning image recognition, determining whether predictions stem from distinct entities or a single entity misidentified, and provides verifiable counterfactual proofs for increased confidence in model interpretations.
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