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Found 6 papers, 1 papers with code
DUE: Dynamic Uncertainty-Aware Explanation Supervision via 3D Imputation
Explanation supervision aims to enhance deep learning models by integrating additional signals to guide the generation of model explanations, showcasing notable improvements in both the predictability and explainability of the model.
XAI Benchmark for Visual Explanation
Our proposed benchmarks facilitate a fair evaluation and comparison of visual explanation methods.
Visual Attention-Prompted Prediction and Learning
To tackle these challenges, we propose a novel framework called Visual Attention-Prompted Prediction and Learning, which seamlessly integrates visual attention prompts into the model's decision-making process and adapts to images both with and without attention prompts for prediction.
MAGI: Multi-Annotated Explanation-Guided Learning
This technique aims to improve the predictability of the model by incorporating human understanding of the prediction process into the training phase.
Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning
As the societal impact of Deep Neural Networks (DNNs) grows, the goals for advancing DNNs become more complex and diverse, ranging from improving a conventional model accuracy metric to infusing advanced human virtues such as fairness, accountability, transparency (FaccT), and unbiasedness.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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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.
