{"list":"other","total":91,"rows":[{"id":"1801.03924","title":"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric","created":"2018-01-11","cited_by_count":11177},{"id":"1610.02391","title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization","created":"2016-10-07","cited_by_count":6733},{"id":"1705.07874","title":"A Unified Approach to Interpreting Model Predictions","created":"2017-05-22","cited_by_count":6555},{"id":"1706.06083","title":"Towards Deep Learning Models Resistant to Adversarial Attacks","created":"2017-06-19","cited_by_count":6415},{"id":"1602.04938","title":"\"Why Should I Trust You?\": Explaining the Predictions of Any Classifier","created":"2016-02-16","cited_by_count":5070},{"id":"1703.01365","title":"Axiomatic Attribution for Deep Networks","created":"2017-03-04","cited_by_count":3384},{"id":"1512.04150","title":"Learning Deep Features for Discriminative Localization","created":"2015-12-14","cited_by_count":2436},{"id":"1312.6034","title":"Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps","created":"2013-12-20","cited_by_count":2371},{"id":"1311.2901","title":"Visualizing and Understanding Convolutional Networks","created":"2013-11-12","cited_by_count":2290},{"id":"1704.02685","title":"Learning Important Features Through Propagating Activation Differences","created":"2017-04-10","cited_by_count":1458},{"id":"1702.08608","title":"Towards A Rigorous Science of Interpretable Machine Learning","created":"2017-02-28","cited_by_count":1416},{"id":"1711.11279","title":"Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)","created":"2017-11-30","cited_by_count":1319},{"id":"1511.04599","title":"DeepFool: a simple and accurate method to fool deep neural networks","created":"2015-11-14","cited_by_count":1318},{"id":"1810.03292","title":"Sanity Checks for Saliency Maps","created":"2018-10-08","cited_by_count":1156},{"id":"1706.03825","title":"SmoothGrad: removing noise by adding noise","created":"2017-06-12","cited_by_count":1109},{"id":"1412.6806","title":"Striving for Simplicity: The All Convolutional Net","created":"2014-12-21","cited_by_count":990},{"id":"1710.11063","title":"Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks","created":"2017-10-30","cited_by_count":939},{"id":"1806.07421","title":"RISE: Randomized Input Sampling for Explanation of Black-box Models","created":"2018-06-19","cited_by_count":838},{"id":"1704.03296","title":"Interpretable Explanations of Black Boxes by Meaningful Perturbation","created":"2017-04-11","cited_by_count":612},{"id":"2002.11569","title":"Overfitting in adversarially robust deep learning","created":"2020-02-26","cited_by_count":506},{"id":"1806.07538","title":"Towards Robust Interpretability with Self-Explaining Neural Networks","created":"2018-06-20","cited_by_count":505},{"id":"1801.00553","title":"Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey","created":"2018-01-02","cited_by_count":460},{"id":"1710.10547","title":"Interpretation of Neural Networks is Fragile","created":"2017-10-29","cited_by_count":444},{"id":"1911.02508","title":"Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods","created":"2019-11-06","cited_by_count":443},{"id":"1509.06321","title":"Evaluating the visualization of what a Deep Neural Network has learned","created":"2015-09-21","cited_by_count":419},{"id":"1907.07374","title":"A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI","created":"2019-07-17","cited_by_count":404},{"id":"2103.11251","title":"Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges","created":"2021-03-20","cited_by_count":367},{"id":"1711.00867","title":"The (Un)reliability of saliency methods","created":"2017-11-02","cited_by_count":333},{"id":"1608.00507","title":"Top-down Neural Attention by Excitation Backprop","created":"2016-08-01","cited_by_count":321},{"id":"1806.08049","title":"On the Robustness of Interpretability Methods","created":"2018-06-21","cited_by_count":314},{"id":"1901.09392","title":"On the (In)fidelity and Sensitivity for Explanations","created":"2019-01-27","cited_by_count":310},{"id":"2006.11371","title":"Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey","created":"2020-06-16","cited_by_count":254},{"id":"1705.07857","title":"Real Time Image Saliency for Black Box Classifiers","created":"2017-05-22","cited_by_count":246},{"id":"2011.07876","title":"A Survey on the Explainability of Supervised Machine Learning","created":"2020-11-16","cited_by_count":227},{"id":"1906.07983","title":"Explanations can be manipulated and geometry is to blame","created":"2019-06-19","cited_by_count":216},{"id":"1604.00825","title":"Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers","created":"2016-04-04","cited_by_count":199},{"id":"2005.00631","title":"Evaluating and Aggregating Feature-based Model Explanations","created":"2020-05-01","cited_by_count":188},{"id":"1811.09716","title":"Robustness via curvature regularization, and vice versa","created":"2018-11-23","cited_by_count":175},{"id":"1812.10528","title":"Adversarial Attack and Defense on Graph Data: A Survey","created":"2018-12-26","cited_by_count":167},{"id":"2108.00401","title":"Advances in adversarial attacks and defenses in computer vision: A survey","created":"2021-08-01","cited_by_count":127},{"id":"1902.02041","title":"Fooling Neural Network Interpretations via Adversarial Model Manipulation","created":"2019-02-06","cited_by_count":112},{"id":"1911.06473","title":"\"How do I fool you?\": Manipulating User Trust via Misleading Black Box Explanations","created":"2019-11-15","cited_by_count":111},{"id":"1905.04172","title":"On the Connection Between Adversarial Robustness and Saliency Map Interpretability","created":"2019-05-10","cited_by_count":80},{"id":"1902.02302","title":"Neural Network Attributions: A Causal Perspective","created":"2019-02-06","cited_by_count":77},{"id":"1812.00891","title":"Interpretable Deep Learning under Fire","created":"2018-12-03","cited_by_count":74},{"id":"1805.12233","title":"How Important Is a Neuron?","created":"2018-05-30","cited_by_count":73},{"id":"2007.00753","title":"Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey","created":"2020-07-01","cited_by_count":72},{"id":"2104.00950","title":"Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey","created":"2021-04-02","cited_by_count":72},{"id":"1805.05492","title":"Did the Model Understand the Question?","created":"2018-05-14","cited_by_count":60},{"id":"2103.01498","title":"A Survey On Universal Adversarial Attack","created":"2021-03-02","cited_by_count":59},{"id":"2203.06877","title":"Rethinking Stability for Attribution-based Explanations","created":"2022-03-14","cited_by_count":52},{"id":"2006.00442","title":"Evaluations and Methods for Explanation through Robustness Analysis","created":"2020-05-31","cited_by_count":51},{"id":"1905.09957","title":"Robust Attribution Regularization","created":"2019-05-23","cited_by_count":47},{"id":"2102.10618","title":"Towards the Unification and Robustness of Perturbation and Gradient Based Explanations","created":"2021-02-21","cited_by_count":47},{"id":"2003.08747","title":"IROF: a low resource evaluation metric for explanation methods","created":"2020-03-09","cited_by_count":46},{"id":"2105.07878","title":"A Review on Explainability in Multimodal Deep Neural Nets","created":"2021-05-17","cited_by_count":46},{"id":"2012.10425","title":"Towards Robust Explanations for Deep Neural Networks","created":"2020-12-18","cited_by_count":43},{"id":"2206.12227","title":"Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective","created":"2022-06-24","cited_by_count":42},{"id":"2006.14748","title":"Proper Network Interpretability Helps Adversarial Robustness in Classification","created":"2020-06-26","cited_by_count":41},{"id":"2103.02354","title":"Evaluating Robustness of Counterfactual Explanations","created":"2021-03-03","cited_by_count":41},{"id":"1810.03307","title":"Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values","created":"2018-10-08","cited_by_count":40},{"id":"2101.09429","title":"Explainable Artificial Intelligence Approaches: A Survey","created":"2021-01-23","cited_by_count":40},{"id":"2006.06643","title":"Smoothed Geometry for Robust Attribution","created":"2020-06-11","cited_by_count":38},{"id":"1906.10670","title":"Improving performance of deep learning models with axiomatic attribution priors and expected gradients","created":"2019-06-25","cited_by_count":32},{"id":"2008.02766","title":"Assessing the (Un)Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging","created":"2020-08-06","cited_by_count":32},{"id":"1802.03788","title":"Influence-Directed Explanations for Deep Convolutional Networks","created":"2018-02-11","cited_by_count":31},{"id":"2108.04990","title":"Perturbing Inputs for Fragile Interpretations in Deep Natural Language Processing","created":"2021-08-11","cited_by_count":30},{"id":"2005.08087","title":"Universal Adversarial Perturbations: A Survey","created":"2020-05-16","cited_by_count":28},{"id":"2009.04521","title":"How Good is your Explanation? Algorithmic Stability Measures to Assess the Quality of Explanations for Deep Neural Networks","created":"2020-09-07","cited_by_count":27},{"id":"2209.14262","title":"A Survey on Physical Adversarial Attack in Computer Vision","created":"2022-09-28","cited_by_count":25},{"id":"2007.06381","title":"A simple defense against adversarial attacks on heatmap explanations","created":"2020-07-13","cited_by_count":23},{"id":"2008.04094","title":"Adversarial Examples on Object Recognition: A Comprehensive Survey","created":"2020-08-07","cited_by_count":23},{"id":"2203.06414","title":"A Survey of Adversarial Defences and Robustness in NLP","created":"2022-03-12","cited_by_count":23},{"id":"2103.11257","title":"Robust Models Are More Interpretable Because Attributions Look Normal","created":"2021-03-20","cited_by_count":21},{"id":"2105.12837","title":"Fooling Partial Dependence via Data Poisoning","created":"2021-05-26","cited_by_count":16},{"id":"2010.07393","title":"FAR: A General Framework for Attributional Robustness","created":"2020-10-14","cited_by_count":13},{"id":"2206.03178","title":"Fooling Explanations in Text Classifiers","created":"2022-06-07","cited_by_count":13},{"id":"2005.14108","title":"Adversarial Attacks and Defense on Texts: A Survey","created":"2020-05-28","cited_by_count":12},{"id":"2012.14395","title":"Enhanced Regularizers for Attributional Robustness","created":"2020-12-28","cited_by_count":12},{"id":"2006.07828","title":"On Saliency Maps and Adversarial Robustness","created":"2020-06-14","cited_by_count":11},{"id":"2002.00526","title":"DANCE: Enhancing saliency maps using decoys","created":"2020-02-03","cited_by_count":10},{"id":"2107.01561","title":"Certifiably Robust Interpretation via Renyi Differential Privacy","created":"2021-07-04","cited_by_count":10},{"id":"2205.13042","title":"How explainable are adversarially-robust CNNs?","created":"2022-05-25","cited_by_count":9},{"id":"1902.07285","title":"Towards a Robust Deep Neural Network in Texts: A Survey","created":"2019-02-12","cited_by_count":7},{"id":"1808.05054","title":"Shedding Light on Black Box Machine Learning Algorithms: Development of an Axiomatic Framework to Assess the Quality of Methods that Explain Individual Predictions","created":"2018-08-15","cited_by_count":6},{"id":"2106.07221","title":"Certification of embedded systems based on Machine Learning: A survey","created":"2021-06-14","cited_by_count":6},{"id":"2205.07279","title":"Exploiting the Relationship Between Kendall's Rank Correlation and Cosine Similarity for Attribution Protection","created":"2022-05-15","cited_by_count":6},{"id":"2007.11709","title":"Adversarial Attacks against Face Recognition: A Comprehensive Study","created":"2020-07-22","cited_by_count":5},{"id":"2104.09073","title":"Improving Attribution Methods by Learning Submodular Functions","created":"2021-04-19","cited_by_count":5},{"id":"1810.07339","title":"Security Matters: A Survey on Adversarial Machine Learning","created":"2018-10-16","cited_by_count":3},{"id":"2112.03615","title":"Saliency Diversified Deep Ensemble for Robustness to Adversaries","created":"2021-12-07","cited_by_count":2}],"next_cursor":null}