{"list":"other","total":74,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1409.0575","title":"ImageNet Large Scale Visual Recognition Challenge","created":"2014-09-01","cited_by_count":8498},{"id":"1312.6199","title":"Intriguing properties of neural networks","created":"2013-12-21","cited_by_count":5155},{"id":"2201.03545","title":"A ConvNet for the 2020s","created":"2022-01-10","cited_by_count":3579},{"id":"1903.12261","title":"Benchmarking Neural Network Robustness to Common Corruptions and Perturbations","created":"2019-03-28","cited_by_count":2877},{"id":"2004.07780","title":"Shortcut Learning in Deep Neural Networks","created":"2020-04-16","cited_by_count":1969},{"id":"2005.10242","title":"Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere","created":"2020-05-20","cited_by_count":1405},{"id":"2007.04612","title":"Concept Bottleneck Models","created":"2020-07-09","cited_by_count":1032},{"id":"2103.17239","title":"Going deeper with Image Transformers","created":"2021-03-31","cited_by_count":764},{"id":"1806.10574","title":"This Looks Like That: Deep Learning for Interpretable Image Recognition","created":"2018-06-27","cited_by_count":737},{"id":"1908.07442","title":"TabNet: Attentive Interpretable Tabular Learning","created":"2019-08-20","cited_by_count":648},{"id":"1710.10547","title":"Interpretation of Neural Networks is Fragile","created":"2017-10-29","cited_by_count":444},{"id":"2211.11158","title":"Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classification","created":"2022-11-21","cited_by_count":312},{"id":"1901.04592","title":"Interpretable machine learning: definitions, methods, and applications","created":"2019-01-14","cited_by_count":305},{"id":"2304.06129","title":"Label-Free Concept Bottleneck Models","created":"2023-04-12","cited_by_count":285},{"id":"1904.00760","title":"Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet","created":"2019-03-20","cited_by_count":260},{"id":"1710.04806","title":"Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions","created":"2017-10-13","cited_by_count":234},{"id":"2103.10158","title":"TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation","created":"2021-03-18","cited_by_count":227},{"id":"1908.02288","title":"BCN20000: Dermoscopic Lesions in the Wild","created":"2019-08-06","cited_by_count":225},{"id":"1906.07983","title":"Explanations can be manipulated and geometry is to blame","created":"2019-06-19","cited_by_count":216},{"id":"2012.02046","title":"Neural Prototype Trees for Interpretable Fine-grained Image Recognition","created":"2020-12-03","cited_by_count":214},{"id":"2111.09259","title":"Acquisition of Chess Knowledge in AlphaZero","created":"2021-11-17","cited_by_count":123},{"id":"2103.10689","title":"Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyond","created":"2021-03-19","cited_by_count":118},{"id":"1902.02041","title":"Fooling Neural Network Interpretations via Adversarial Model Manipulation","created":"2019-02-06","cited_by_count":112},{"id":"2112.03184","title":"HIVE: Evaluating the Human Interpretability of Visual Explanations","created":"2021-12-06","cited_by_count":105},{"id":"2112.02902","title":"Interpretable Image Classification with Differentiable Prototypes Assignment","created":"2021-12-06","cited_by_count":97},{"id":"2011.14340","title":"ProtoPShare: Prototype Sharing for Interpretable Image Classification and Similarity Discovery","created":"2020-11-29","cited_by_count":89},{"id":"2007.09969","title":"Fairwashing Explanations with Off-Manifold Detergent","created":"2020-07-20","cited_by_count":72},{"id":"2105.02968","title":"This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep Networks","created":"2021-05-05","cited_by_count":65},{"id":"2302.14260","title":"A Closer Look at the Intervention Procedure of Concept Bottleneck Models","created":"2023-02-28","cited_by_count":64},{"id":"2208.10431","title":"ProtoPFormer: Concentrating on Prototypical Parts in Vision Transformers for Interpretable Image Recognition","created":"2022-08-22","cited_by_count":63},{"id":"2205.15769","title":"Concept-level Debugging of Part-Prototype Networks","created":"2022-05-31","cited_by_count":52},{"id":"2011.02863","title":"This Looks Like That, Because ... Explaining Prototypes for Interpretable Image Recognition","created":"2020-11-05","cited_by_count":50},{"id":"2308.06248","title":"FunnyBirds: A Synthetic Vision Dataset for a Part-Based Analysis of Explainable AI Methods","created":"2023-08-11","cited_by_count":50},{"id":"2306.06123","title":"Adversarial attacks and defenses in explainable artificial intelligence: A survey","created":"2023-06-06","cited_by_count":48},{"id":"2207.09615","title":"Overlooked factors in concept-based explanations: Dataset choice, concept learnability, and human capability","created":"2022-07-20","cited_by_count":46},{"id":"2212.05946","title":"Evaluation and Improvement of Interpretability for Self-Explainable Part-Prototype Networks","created":"2022-12-12","cited_by_count":37},{"id":"2401.14142","title":"Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Probabilistic Interpretations","created":"2024-01-25","cited_by_count":33},{"id":"2306.10898","title":"B-cos Alignment for Inherently Interpretable CNNs and Vision Transformers","created":"2023-06-19","cited_by_count":31},{"id":"2303.16047","title":"Exploring and Interacting with the Set of Good Sparse Generalized Additive Models","created":"2023-03-28","cited_by_count":30},{"id":"2208.09418","title":"SAFARI: Versatile and Efficient Evaluations for Robustness of Interpretability","created":"2022-08-19","cited_by_count":28},{"id":"2308.08162","title":"Interpretability Benchmark for Evaluating Spatial Misalignment of Prototypical Parts Explanations","created":"2023-08-16","cited_by_count":27},{"id":"2109.00520","title":"The Role of Explainability in Assuring Safety of Machine Learning in Healthcare","created":"2021-09-01","cited_by_count":26},{"id":"2410.20722","title":"Interpretable Image Classification with Adaptive Prototype-based Vision Transformers","created":"2024-10-28","cited_by_count":25},{"id":"2108.10612","title":"ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image Classification","created":"2021-08-24","cited_by_count":22},{"id":"2211.16080","title":"Understanding and Enhancing Robustness of Concept-based Models","created":"2022-11-29","cited_by_count":22},{"id":"2401.15866","title":"Stochastic Amortization: A Unified Approach to Accelerate Feature and Data Attribution","created":"2024-01-29","cited_by_count":21},{"id":"2402.13914","title":"Position: Explain to Question not to Justify","created":"2024-02-21","cited_by_count":20},{"id":"2105.06791","title":"Agree to Disagree: When Deep Learning Models With Identical Architectures Produce Distinct Explanations","created":"2021-05-14","cited_by_count":15},{"id":"2405.14331","title":"LucidPPN: Unambiguous Prototypical Parts Network for User-centric Interpretable Computer Vision","created":"2024-05-23","cited_by_count":15},{"id":"2206.13872","title":"When are Post-hoc Conceptual Explanations Identifiable?","created":"2022-06-28","cited_by_count":14},{"id":"1907.10901","title":"How to Manipulate CNNs to Make Them Lie: the GradCAM Case","created":"2019-07-25","cited_by_count":12},{"id":"2411.00715","title":"B-cosification: Transforming Deep Neural Networks to be Inherently Interpretable","created":"2024-11-01","cited_by_count":12},{"id":"2307.10404","title":"Interpreting and Correcting Medical Image Classification with PIP-Net","created":"2023-07-19","cited_by_count":11},{"id":"2406.00426","title":"InterpreTabNet: Distilling Predictive Signals from Tabular Data by Salient Feature Interpretation","created":"2024-06-01","cited_by_count":10},{"id":"2206.15182","title":"The (de)biasing effect of GAN-based augmentation methods on skin lesion images","created":"2022-06-30","cited_by_count":9},{"id":"2302.13356","title":"Performance is not enough: the story told by a Rashomon quartet","created":"2023-02-26","cited_by_count":9},{"id":"2311.16120","title":"Sanity checks for patch visualisation in prototype-based image classification","created":"2023-10-25","cited_by_count":8},{"id":"2406.10025","title":"ProtoS-ViT: Visual foundation models for sparse self-explainable classifications","created":"2024-06-14","cited_by_count":8},{"id":"2407.11910","title":"Benchmarking the Attribution Quality of Vision Models","created":"2024-07-16","cited_by_count":8},{"id":"2211.01498","title":"On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach","created":"2022-11-02","cited_by_count":7},{"id":"2303.09817","title":"Interpretable machine learning for time-to-event prediction in medicine and healthcare","created":"2023-03-17","cited_by_count":7},{"id":"2407.08947","title":"Constructing Concept-based Models to Mitigate Spurious Correlations with Minimal Human Effort","created":"2024-07-12","cited_by_count":7},{"id":"2306.02312","title":"(Un)reasonable Allure of Ante-hoc Interpretability for High-stakes Domains: Transparency Is Necessary but Insufficient for Comprehensibility","created":"2023-06-04","cited_by_count":6},{"id":"2407.16653","title":"Aggregated Attributions for Explanatory Analysis of 3D Segmentation Models","created":"2024-07-23","cited_by_count":5},{"id":"2404.01964","title":"CAM-Based Methods Can See through Walls","created":"2024-04-02","cited_by_count":4},{"id":"2404.02067","title":"Red-Teaming Segment Anything Model","created":"2024-04-02","cited_by_count":4},{"id":"2406.18334","title":"Efficient and Accurate Explanation Estimation with Distribution Compression","created":"2024-06-26","cited_by_count":4},{"id":"2311.06647","title":"Robust Text Classification: Analyzing Prototype-Based Networks","created":"2023-11-11","cited_by_count":3},{"id":"2403.18328","title":"PIPNet3D: Interpretable Detection of Alzheimer in MRI Scans","created":"2024-03-27","cited_by_count":3},{"id":"2310.17010","title":"This Reads Like That: Deep Learning for Interpretable Natural Language Processing","created":"2023-10-25","cited_by_count":2},{"id":"2406.09069","title":"On the Robustness of Global Feature Effect Explanations","created":"2024-06-13","cited_by_count":2},{"id":"2407.14277","title":"Patch-based Intuitive Multimodal Prototypes Network (PIMPNet) for Alzheimer's Disease classification","created":"2024-07-19","cited_by_count":2},{"id":"2408.11401","title":"Revisiting FunnyBirds evaluation framework for prototypical parts networks","created":"2024-08-21","cited_by_count":2}],"next_cursor":null}