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UFO: A unified method for controlling Understandability and Faithfulness Objectives in concept-based explanations for CNNs.

, , , и . CoRR, (2023)

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Другие публикации лиц с тем же именем

Fair Attribute Classification Through Latent Space De-Biasing., , и . CVPR, стр. 9301-9310. Computer Vision Foundation / IEEE, (2021)"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction., , , , и . CoRR, (2022)Fair Attribute Classification through Latent Space De-biasing., , и . CoRR, (2020)Establishing Appropriate Trust in AI through Transparency and Explainability.. CHI Extended Abstracts, стр. 433:1-433:6. ACM, (2024)Allowing humans to interactively guide machines where to look does not always improve human-AI team's classification accuracy., , , и . CoRR, (2024)Overlooked factors in concept-based explanations: Dataset choice, concept salience, and human capability., , , и . CoRR, (2022)UFO: A unified method for controlling Understandability and Faithfulness Objectives in concept-based explanations for CNNs., , , и . CoRR, (2023)ELUDE: Generating interpretable explanations via a decomposition into labelled and unlabelled features., , , , и . CoRR, (2022)Humans, AI, and Context: Understanding End-Users' Trust in a Real-World Computer Vision Application., , , , и . FAccT, стр. 77-88. ACM, (2023)Overlooked Factors in Concept-Based Explanations: Dataset Choice, Concept Learnability, and Human Capability., , , и . CVPR, стр. 10932-10941. IEEE, (2023)