{"total":21,"rows":[{"id":"2111.14338","title":"Improving Deep Learning Interpretability by Saliency Guided Training","created":"2021-11-29","cited_by_count":68,"area":null},{"id":"2207.14526","title":"Leveraging Explanations in Interactive Machine Learning: An Overview","created":"2022-07-29","cited_by_count":53,"area":null},{"id":"2212.03954","title":"Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning","created":"2022-12-07","cited_by_count":36,"area":null},{"id":"2106.01127","title":"Towards Robust Classification Model by Counterfactual and Invariant Data Generation","created":"2021-06-02","cited_by_count":30,"area":null},{"id":"2106.01809","title":"Exploring Distantly-Labeled Rationales in Neural Network Models","created":"2021-06-03","cited_by_count":8,"area":null},{"id":"2210.16978","title":"XMD: An End-to-End Framework for Interactive Explanation-Based Debugging of NLP Models","created":"2022-10-30","cited_by_count":8,"area":null},{"id":"2309.08035","title":"Interpretability-Aware Vision Transformer","created":"2023-09-14","cited_by_count":8,"area":null},{"id":"2310.00772","title":"SMOOT: Saliency Guided Mask Optimized Online Training","created":"2023-10-01","cited_by_count":8,"area":null},{"id":"2109.03756","title":"Diagnostics-Guided Explanation Generation","created":"2021-09-08","cited_by_count":6,"area":null},{"id":"2205.12542","title":"ER-Test: Evaluating Explanation Regularization Methods for Language Models","created":"2022-05-25","cited_by_count":6,"area":null},{"id":"2404.03098","title":"Exploring the Trade-off Between Model Performance and Explanation Plausibility of Text Classifiers Using Human Rationales","created":"2024-04-03","cited_by_count":5,"area":null},{"id":"2310.14418","title":"REFER: An End-to-end Rationale Extraction Framework for Explanation Regularization","created":"2023-10-22","cited_by_count":4,"area":null},{"id":"2205.04652","title":"SuMe: A Dataset Towards Summarizing Biomedical Mechanisms","created":"2022-05-10","cited_by_count":2,"area":null},{"id":"2311.05143","title":"SCAAT: Improving Neural Network Interpretability via Saliency Constrained Adaptive Adversarial Training","created":"2023-11-09","cited_by_count":2,"area":null},{"id":"2404.14996","title":"CA-Stream: Attention-based pooling for interpretable image recognition","created":"2024-04-23","cited_by_count":2,"area":null},{"id":"2409.17370","title":"The Overfocusing Bias of Convolutional Neural Networks: A Saliency-Guided Regularization Approach","created":"2024-09-25","cited_by_count":2,"area":null},{"id":"2407.16693","title":"Explanation Regularisation through the Lens of Attributions","created":"2024-07-23","cited_by_count":1,"area":null},{"id":"2306.16581","title":"Does Saliency-Based Training bring Robustness for Deep Neural Networks in Image Classification?","created":"2023-06-28","cited_by_count":0,"area":null},{"id":"2404.02068","title":"Using Interpretation Methods for Model Enhancement","created":"2024-04-02","cited_by_count":0,"area":null},{"id":"2506.07168","title":"Efficient Text-Attributed Graph Learning through Selective Annotation and Graph Alignment","created":"2025-06-08","cited_by_count":0,"area":null},{"id":"2604.25315","title":"SaliencyDecor: Enhancing Neural Network Interpretability through Feature Decorrelation","created":"2026-04-28","cited_by_count":0,"area":null}],"next_cursor":null}