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ImageNet classification with deep convolutional neural networks

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ImageNet Large Scale Visual Recognition Challenge

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Learning representations by back-propagating errors

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Learning Multiple Layers of Features from Tiny Images

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Visualizing and understanding convolutional networks

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Explaining and Harnessing Adversarial Examples

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An introduction to variable and feature selection

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Domain-Adversarial Training of Neural Networks

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Intriguing properties of neural networks

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Transferring Information Between Neural Networks

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Multi-Convolution Neural Networks-Based Deep Learning Model for Emotion Understanding

Chen, L., Wu, M., Su, W., & Hirota, K. (2018) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2018.8483607 · Cited by 3

Logic Negation with Spiking Neural P Systems

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Data-Independent Feature Learning with Markov Random Fields in Convolutional Neural Networks

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Large Scale Evaluation of Importance Maps in Automatic Speech Recognition

Trinh, V. A., & Mandel, M. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2883 · Cited by 3

A self-explanatory contrastive logical knowledge learning method for sentiment analysis

Chen, Y., Yuan, B., Liao, B., & Gabbay, D. M. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110863 · Cited by 3

Why Lethal autonomous weapon systems are unacceptable

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Improving the Performance and Explainability of Mammogram Classifiers with Local Annotations

Ness, L., Barkan, E., & Ozery-Flato, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61166-8_4 · Cited by 3

Visual Explanation by Unifying Adversarial Generation and Feature Importance Attributions

Charachon, M., Cournède, P.-H., Hudelot, C., & Ardon, R. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87444-5_5 · Cited by 4

Computational Methods and Deep Learning for Elucidating Protein Interaction Networks

Vora, D. S., Kalakoti, Y., & Sundar, D. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2617-7_15 · Cited by 3

The Implication of Deep Neural Networks in Solving Optimization Problems for Network Security

Hassan, S. (2020) · International Journal of Computer Applications · doi:10.5120/ijca2020920154 · Cited by 3

Assessing the Reliability of Visual Explanations of Deep Models with Adversarial Perturbations

Valle, D., Pimentel, T., & Veloso, A. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9206795 · Cited by 3

VisualBackProp for learning using privileged information with CNNsPreprint

Bisla, D., & Choromanska, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.09474 · Cited by 6

Understanding Patch-Based Learning by Explaining PredictionsPreprint

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Introducing and assessing the explainable AI (XAI)method: SIDUPreprint

Muddamsetty, S. M., Jahromi, M. N. S., Ciontos, A. E., Fenoy, L. M., & Moeslund, T. B. (2021) · arXiv (Cornell University) · Cited by 3

GANMEX: One-vs-One Attributions Guided by GAN-based Counterfactual Explanation BaselinesPreprint

Shih, S.-M., Tien, P.-J., & Karnin, Z. S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.06015 · Cited by 3

Quality Metrics for Transparent Machine Learning With and Without Humans In the Loop Are Not CorrelatedPreprint

Bießmann, F., & Refiano, D. I. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.02033 · Cited by 3

Robust Malware Detection using Residual Attention NetworkPreprint

Ganesan, S., Vinayakumar, R., Krichen, M., Sowmya, V., & Soman, K. (2020) · doi:10.36227/techrxiv.13385291.v1 · Cited by 3

A Review of the Convergence Between Explainable Artificial Intelligence and Multi-Objective OptimizationPreprint

Monteiro, W. R., & Reynoso-Meza, G. (2022) · doi:10.36227/techrxiv.21707261 · Cited by 3

ASMOD: Adaptive Saliency Map on Object Detection

Xu, Z., Jiang, Y., Li, G., & Zhu, R. (2022) · IEEE International Conference on Information, Communication and Networks (ICICN) · doi:10.1109/icicn56848.2022.10006535 · Cited by 2

Neural dynamics of robust legged robots

Rush, E. R., Heckman, C., Jayaram, K., & Humbert, J. S. (2024) · Frontiers in Robotics and AI · doi:10.3389/frobt.2024.1324404 · Cited by 2

A Guided Tour of Post-hoc XAI Techniques in Image Segmentation

Hasany, S. N., Mériaudeau, F., & Petitjean, C. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-63797-1_9 · Cited by 3

Fast gradient-free activation maximization for neurons in spiking neural networks

Pospelov, N., Chertkov, A., Beketov, M., Oseledets, I. V., & Anokhin, K. V. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.129070 · Cited by 3

SA-CAM: Semantic-aware visual explanations for deep convolutional networks

Yu, A., Zhang, Q., Rao, L., & Yang, Y.-B. (2025) · Machine Learning · doi:10.1007/s10994-024-06688-8 · Cited by 2

IMPACTX: improving model performance by appropriately constraining the training with teacher explanations

Apicella, A., Giugliano, S., Isgrò, F., Pollastro, A., & Prevete, R. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11564-z · Cited by 2

Weakly supervised lesion localization and attribution for OCT images with a guided counterfactual explainer model

Jiang, L., Xie, R., Yang, B., He, J., Huang, H., Pan, Y. F., & Cai, Y. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128129 · Cited by 3

TIPS: A text interaction evaluation metric for learning model interpretation

Nie, Z., Xiao, Z., Wang, T., Chronopoulos, A. T., Andonie, R., & Mosavi, A. A. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128184 · Cited by 2

Interpretable named entity recognition via integrating logical rule learning with deep neural networks

Chen, Y., Yuan, B., Liao, B., Gabbay, D. M., & Cheng, L. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129337 · Cited by 2

Assessing the noise robustness of Class Activation Maps: A framework for reliable model interpretability

Sarkar, S., Bora, R. P., Kaushal, B., George, S. N., & Raja, K. B. (2025) · Image and Vision Computing · doi:10.1016/j.imavis.2025.105717 · Cited by 2

DQSA: Dynamic Quantized Self-Attention for Multi-Task Encrypted Network Traffic Classification

Chen, Y., Lan, H., Gwee, B., Cao, Q. P., Razul, S. G., & Lin, Z. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3647211 · Cited by 2

Deep learning for predicting stem cell efficiency for use in beta cell differentiation

Schob, F., Binder, A., Zamarian, V., Sordi, V., Scholz, H., Malthe-Sørenssen, A., & Dysthe, D. K. (2026) · Scientific Reports · doi:10.1038/s41598-026-42830-3 · Cited by 2

A clinically-oriented XAI framework for arrhythmia triage via Hierarchically-Decomposed Neural Attribution: From dual-stream interpretation to risk stratification

Wu, Y., Zhu, J., Yuan, J., & Huang, K. (2026) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2026.108991 · Cited by 2

Deep learning models for comprehensive landslide susceptibility study considering climate change and sustainable development implications in Western Province of Rwanda

Nwazelibe, V. E., Abdulkarim, M., Agbasi, J. C., Okpala, E. C., Pande, C. B., Abba, S. I., & Egbueri, J. C. (2026) · Natural Hazards · doi:10.1007/s11069-026-08182-x · Cited by 2

A quantitative framework for explainability assessment in medical imaging

Fanale, R., Caldelli, R., Martini, B., & Sciarrone, F. (2026) · Frontiers in Imaging · doi:10.3389/fimag.2026.1846414 · Cited by 2

Explainable AI for Deep Visual Recognition: Evaluation, Methods, and Open Challenges

Alharbi, K. N. (2026) · Electronics · doi:10.3390/electronics15143222 · Cited by 2

AnchorMoE: Interpretable Time Series Classification via Anchor-Routed MoE

Xie, T., Tan, Z., Xiao, H., Li, M., Zhang, Y., Lu, Y., Yang, C., & Cheung, Y. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3817786 · Cited by 2

Erklärbare KI in der medizinischen Diagnose – Erfolge und Herausforderungen

Lucieri, A., Bajwa, M. N., Dengel, A. R., & Ahmed, S. (2022) · Springer eBooks · doi:10.1007/978-3-658-33597-7_35 · Cited by 2

SSA-CAE-Based Abnormal Data Classification Method in Edge Intelligence Device of CNC Machine

Kim, D., Oh, S., Lee, J., & Jeong, J. (2022) · Applied Sciences · doi:10.3390/app12125864 · Cited by 2

A maximum-entropy-attention-based convolutional neural network for image perception

Chen, Q., Zhang, A., & Pan, G. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07564-z · Cited by 2

Research on Music Genre Recognition Based on Improved Naive Bayes Algorithm

Liu, X. (2022) · Mobile Information Systems · doi:10.1155/2022/1909928 · Cited by 2

Electronic Synaptic Devices with High Thermostability Induced by Embedded Tungsten Disulfide Quantum Dots for Machine Learning

An, H., An, J. S., Li, M.-J., Kim, Y., & Kim, T. W. (2022) · Advanced Electronic Materials · doi:10.1002/aelm.202200876 · Cited by 2

A Systematic Approach for Tracking the Evolution of XAI as a Field of Research

Jamshidi, S., Nowaczyk, S., Fanaee‐T, H., & Rahat, M. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-23633-4_31 · Cited by 2

Hierarchical multi-scale parametric optimization of deep neural networks

Zhang, S., Vassiliadis, V. S., Dorneanu, B., & Arellano‐García, H. (2023) · Applied Intelligence · doi:10.1007/s10489-023-04745-8 · Cited by 2

Maximum Entropy Baseline for Integrated Gradients

Tan, H. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191554 · Cited by 2

Feature Importance for Clustering

Nápoles, G., Griffioen, N., Khoshrou, S., & Güven, Ç. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-49018-7_3 · Cited by 2

Selective Presentation of AI Object Detection Results While Maintaining Human Reliance

Fukuchi, Y., & Yamada, S. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341684 · Cited by 2

Accelerating the Global Aggregation of Local Explanations

Mor, A., Belinkov, Y., & Kimelfeld, B. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29845 · Cited by 3

Quantitative Evaluation of xAI Methods for Multivariate Time Series - A Case Study for a CNN-Based MI Detection Model

Knof, H., Boerger, M., & Tcholtchev, N. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-63803-9_9 · Cited by 2

Locally Testing Model Detections for Semantic Global Concepts

Motzkus, F., Mikriukov, G., Hellert, C., & Schmid, U. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-63787-2_8 · Cited by 2

Meta-evaluating Stability Measures: MAX-Sensitivity and AVG-Sensitivity

Miró-Nicolau, M., Jaume-i-Capó, A., & Moyà-Alcover, G. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-63787-2_18 · Cited by 2

Hidden layer target spatio-temporal correlation feature regression based soft sensing for industrial process

Guo, X., Lu, X., & Li, Y. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad7a14 · Cited by 2

Structured Gradient-Based Interpretations via Norm-Regularized Adversarial Training

Gong, S., Dou, Q., & Farnia, F. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01047 · Cited by 3

Interpretation with baseline shapley value for feature groups on tree models

Xu, F., Zhou, Z., Ni, J., & Gao, W. D. (2024) · Frontiers of Computer Science · doi:10.1007/s11704-024-40117-2 · Cited by 2

Examining the Influenza A Virus Sialic Acid Binding Preference Predictions of a Sequence‐Based Convolutional Neural Network

Borkenhagen, L. K., & Runstadler, J. A. (2024) · Influenza and Other Respiratory Viruses · doi:10.1111/irv.70044 · Cited by 2

SwinLeafExplain: Interpretable Cotton Disease Classification with SWIN Transformer

Mariyappan, S., Thomas, J., & Raj, E. D. (2024) · International Conference on Advances in Computing and Communications (ICACC) · doi:10.1109/icacc63692.2024.10845618 · Cited by 2

Less Is More: The Influence of Pruning on the Explainability of CNNs

Merkle, F., Weber, D., Schöttle, P., Schlögl, S., & Nocker, M. (2025) · IEEE Access · doi:10.1109/access.2025.3569575 · Cited by 9

FRIES: Framework for inconsistency estimation of saliency metrics

Bora, R. P., Terhörst, P., Veldhuis, R. N. J., Ramachandra, R., & Raja, K. B. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.112136 · Cited by 2

Convolutional neural net learns promoter sequence features driving transcription strength

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Evaluation of Importance Estimators in Deep Learning Classifiers for Computed Tomography

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Dividing and Conquering a BlackBox to a Mixture of Interpretable Models: Route, Interpret, Repeat

Ghosh, S., Yu, K., Arabshahi, F., & Batmanghelich, K. N. (2023) · PubMed · doi:10.48550/arxiv.2307.05350 · Cited by 2

Object Boundary Detection and Classification with Image-Level Labels

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Semi-Automatic Reliable Explanations for Prediction in Graphs

Todoriki, M., Shingu, M., Yano, S., Tolmachev, A., Komikado, T., & Maruhashi, K. (2021) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc51732.2021.9375922 · Cited by 2

On the conditions for integrating deep learning into the study of visual politics

Magnani, M., & Segerberg, A. (2021) · ACM Web Science Conference (WebSci) · doi:10.1145/3447535.3462511 · Cited by 2

Improving Explanations of Image Classification with Ensembles of Learners

Ahamed, A., Alipour, K., Kumar, S., Soltani, S., & Pazzani, M. J. (2022) · doi:10.5121/csit.2022.121801 · Cited by 2

Explanation of Multi-Label Neural Networks with Layer-Wise Relevance Propagation

Bello, M., Nápoles, G., Vanhoof, K., García, M. M., & Bello, R. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892239 · Cited by 2

The Solvability of Interpretability Evaluation Metrics

Zhou, Y., & Shah, J. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.182 · Cited by 12

Extensive evaluation of image classifiers’ interpretations

Poštić, S., & Subašić, M. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10273-4 · Cited by 2

Weight-Adapted Convolution Neural Network for Facial Expression Recognition

Chen, L., Wu, M., Witold Pedrycz, C., & Hirota, K. (2020) · Studies in computational intelligence · doi:10.1007/978-3-030-61577-2_5 · Cited by 2

Supporting DNN Safety Analysis and Retraining through Heatmap-based Unsupervised Learning

Fahmy, H. M., Pastore, F., Bagherzadeh, M., & Briand, L. (2021) · Figshare · doi:10.5281/zenodo.4725302 · Cited by 2

Measuring and improving the quality of visual explanationsPreprint

Grabska‐Barwińska, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.08774 · Cited by 2

Explainable Adversarial Attacks in Deep Neural Networks Using Activation ProfilesPreprint

Cantareira, G. D., de Mello, R. F., & Paulovich, F. V. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.10229 · Cited by 9

Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group MasksPreprint

Chen, H., Feng, S., Ganhotra, J., Wan, H., Gunasekara, C., Joshi, S., & Ji, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.04488 · Cited by 2

Reachability Analysis of Convolutional Neural NetworksPreprint

Yang, X.-D., Yamaguchi, T., Tran, H.-D., Hoxha, B., Johnson, T. T., & Prokhorov, D. (2021) · arXiv.org · doi:10.48550/arxiv.2106.12074 · Cited by 6

Discriminative Attribution from CounterfactualsPreprint

Eckstein, N., Bates, A. S., Jefferis, G. S. X. E., & Funke, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.13412 · Cited by 2

Quantifying Trustworthiness of Explainability in Medical AIPreprint

Zhang, J., Chao, H., Dasegowda, G., Wang, G., Kalra, M. K., & Yan, P. (2022) · Research Square · doi:10.21203/rs.3.rs-1897856/v1 · Cited by 2

The worst graph layout algorithm everPreprint

Bartolomeo, S. D., Lang, M., & Dunne, C. (2022) · doi:10.31219/osf.io/4hfy9 · Cited by 2

Artificial Intelligence and Applications

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A Step Towards Generalisability: Training a Machine Learning Scoring Function for Structure-Based Virtual ScreeningPreprint

Scantlebury, J., Vost, L., Carbery, A., Hadfield, T. E., Turnbull, O. M., Brown, N., Chenthamarakshan, V., Das, P., Grosjean, H., von Delft, F., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.28.511712 · Cited by 2

Kernel-based Algorithms for Image Classification: A ReviewPreprint

Liang, X., Du, S., Duan, Y., Chen, Y., Zhu, K., Yin, Y., & Jian, L. (2023) · Research Square · doi:10.21203/rs.3.rs-3576956/v1 · Cited by 3

Evaluating Explainable AI on a Multi-Modal Medical Imaging Task: Can Existing Algorithms Fulfill Clinical Requirements?

Jin, W., Li, X., & Hamarneh, G. (2022) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v36i11.21452 · Cited by 79

Stand-in Backdoor: A Stealthy and Powerful Backdoor Attack

Li, S., Li, H., & Chen, H. (2021) · Globecom · doi:10.1109/globecom46510.2021.9685762 · Cited by 1

Femtosecond pulse parameter estimation from photoelectron momenta using machine learning

Szołdra, T., Ciappina, M. F., Werby, N., Bucksbaum, P. H., Lewenstein, M., Zakrzewski, J., & Maxwell, A. (2023) · New Journal of Physics · doi:10.1088/1367-2630/acee19 · Cited by 1

TIF: Threshold Interception and Fusion for Compact and Fine-Grained Visual Attribution

Zheng, G., Sang, J., & Xu, C. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3324808 · Cited by 1

Improve interpretability of Information Bottlenecks for Attribution with Layer-wise Relevance Propagation

Chen, X., Li, J., Liu, J., Peters, S., Liu, L., Le, T. D., & Walsh, A. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386271 · Cited by 1

PXPermute reveals staining importance in multichannel imaging flow cytometry

Boushehri, S. S., Kornivetc, A., Winter, D. J. E., Kazeminia, S., Essig, K., Schmich, F., & Marr, C. (2024) · Cell Reports Methods · doi:10.1016/j.crmeth.2024.100715 · Cited by 1

Advancing Concrete Segregation Detection: Leveraging Explainable AI (XAI) for Improved Accuracy and Transparency

Matich, H., & Mousannif, H. (2024) · Sustainable civil infrastructures · doi:10.1007/978-3-031-76557-5_5 · Cited by 1

Towards Boosting Out-of-Distribution Detection from a Spatial Feature Importance Perspective

Zhu, Y. T., Yan, X. J., & Xie, C. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02347-0 · Cited by 1

Quantifying Explainability: Essential Guidelines for Evaluating XAI Methods

Djellikh, S., Youkana, I., Ammari, A., & Saouli, R. (2025) · International Symposium on iNnovative Informatics of Biskra (ISNIB) · doi:10.1109/isnib64820.2025.10983231 · Cited by 1

Structural optimization of stiffened panel structures with continuous and discrete design variables using deep reinforcement learning

Nonami, R., & Kitamura, M. (2025) · Transactions of the JSME (in Japanese) · doi:10.1299/transjsme.25-00020 · Cited by 1

Evaluating the Consistency and Reliability of Attribution Methods in Automated Short Answer Grading (ASAG) Systems: Toward an Explainable Scoring System

Pinto, W. N., & Shin, J. (2025) · Journal of Educational Measurement · doi:10.1111/jedm.12438 · Cited by 1

Explaining care need assessment surveys: qualitative and quantitative evaluation of state-of-the-art local and global explainable artificial intelligence methods

Serbetci, O., Blüher, S., Gellert, P. R., & Leser, U. (2025) · JAMIA Open · doi:10.1093/jamiaopen/ooaf064 · Cited by 1

Spectral Occlusion - Attribution Beyond Spatial Relevance Heatmaps

Schmeisser, F., Lucieri, A., Dengel, A. R., & Ahmed, S. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-08333-3_8 · Cited by 1

Explaining word embeddings with perfect fidelity: a case study in predicting research impact

Dvořáčková, L., Joachimiak, M. P., Černý, M., Kubecová, A., Sklenák, V., & Kliegr, T. (2025) · Machine Learning · doi:10.1007/s10994-025-06870-6 · Cited by 2

An Efficient and Explainable KAN Framework for Wireless Radiation Field Prediction

Shen, J., & Wang, X. (2025) · IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (MASS) · doi:10.1109/mass66014.2025.00021 · Cited by 1

A Literature Review on Example-Based Explanations in Medical Image Analysis

Montenegro, H., & Cardoso, J. S. (2025) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-025-00227-3 · Cited by 1

Multilayer machine learning with visual knowledge discovery

Kovalerchuk, B., & Huber, L. (2025) · Information Visualization · doi:10.1177/14738716251363233 · Cited by 1

Rethinking explainable AI: The gap between saliency-based explanation and user understanding for object detection models

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