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Found 2 papers, 2 papers with code
Designing a Direct Feedback Loop between Humans and Convolutional Neural Networks through Local Explanations
To mitigate the gap, we designed DeepFuse, the first interactive design that realizes the direct feedback loop between a user and CNNs in diagnosing and revising CNN's vulnerability using local explanations.
RES: A Robust Framework for Guiding Visual Explanation
Despite the fast progress of explanation techniques in modern Deep Neural Networks (DNNs) where the main focus is handling "how to generate the explanations", advanced research questions that examine the quality of the explanation itself (e. g., "whether the explanations are accurate") and improve the explanation quality (e. g., "how to adjust the model to generate more accurate explanations when explanations are inaccurate") are still relatively under-explored.
