On the public citation graph

Interpretable CNNs for Object Classification

https://doi.org/10.1109/tpami.2020.2982882

0 signed · 119 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-10590-1_53openalex-snapshot

Visualizing and Understanding Convolutional Networks

inferredcites →https://doi.org/10.1007/978-3-319-16808-1_12openalex-snapshot

Part Detector Discovery in Deep Convolutional Neural Networks

inferredcites →https://doi.org/10.1007/978-3-642-33709-3_6openalex-snapshot

Unsupervised Discovery of Mid-Level Discriminative Patches

inferredcites →https://doi.org/10.1109/5.726791openalex-snapshot

Gradient-based learning applied to document recognition

inferredcites →https://doi.org/10.1109/cvpr.2014.136openalex-snapshot

Learning Inhomogeneous FRAME Models for Object Patterns

inferredcites →https://doi.org/10.1109/cvpr.2014.254openalex-snapshot

Detect What You Can: Detecting and Representing Objects Using Holistic Models and Body Parts

inferredcites →https://doi.org/10.1109/cvpr.2015.7299155openalex-snapshot

Understanding deep image representations by inverting them

inferredcites →https://doi.org/10.1109/cvpr.2016.319openalex-snapshot

Learning Deep Features for Discriminative Localization

inferredcites →https://doi.org/10.1109/cvpr.2016.522openalex-snapshot

Inverting Visual Representations with Convolutional Networks

inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot

Deep Residual Learning for Image Recognition

inferredcites →https://doi.org/10.1109/cvpr.2017.354openalex-snapshot

Network Dissection: Quantifying Interpretability of Deep Visual Representations

inferredcites →https://doi.org/10.1109/cvpr.2017.414openalex-snapshot

Mining Object Parts from CNNs via Active Question-Answering

inferredcites →https://doi.org/10.1109/cvpr.2018.00920openalex-snapshot

Interpretable Convolutional Neural Networks

inferredcites →https://doi.org/10.1109/cvpr.2019.00642openalex-snapshot

Interpreting CNNs via Decision Trees

inferredcites →https://doi.org/10.1109/cvpr42600.2020.01294openalex-snapshot

Explaining Knowledge Distillation by Quantifying the Knowledge

inferredcites →https://doi.org/10.1109/cvprw.2017.212openalex-snapshot

Human-Explainable Features for Job Candidate Screening Prediction

inferredcites →https://doi.org/10.1109/cvprw.2017.213openalex-snapshot

Explaining Distributed Neural Activations via Unsupervised Learning

inferredcites →https://doi.org/10.1109/cvprw.2017.215openalex-snapshot

Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks

inferredcites →https://doi.org/10.1109/cvprw.2017.216openalex-snapshot

Decoding the Deep: Exploring Class Hierarchies of Deep Representations Using Multiresolution Matrix Factorization

inferredcites →https://doi.org/10.1109/cvprw.2017.217openalex-snapshot

Interpreting CNN Models for Apparent Personality Trait Regression

inferredcites →https://doi.org/10.1109/iccv.2015.136openalex-snapshot

Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks

inferredcites →https://doi.org/10.1109/iccv.2015.329openalex-snapshot

Understanding Deep Features with Computer-Generated Imagery

inferredcites →https://doi.org/10.1109/iccv.2017.371openalex-snapshot

Interpretable Explanations of Black Boxes by Meaningful Perturbation

inferredcites →https://doi.org/10.1109/iccv.2017.74openalex-snapshot

Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

inferredcites →https://doi.org/10.1109/iccv.2019.00928openalex-snapshot

Explaining Neural Networks Semantically and Quantitatively

inferredcites →https://doi.org/10.1109/tevc.2019.2890858openalex-snapshot

One Pixel Attack for Fooling Deep Neural Networks

inferredcites →https://doi.org/10.1109/tpami.2013.35openalex-snapshot

Learning AND-OR Templates for Object Recognition and Detection

inferredcites →https://doi.org/10.1145/2939672.2939778openalex-snapshot

"Why Should I Trust You?"

inferredcites →https://doi.org/10.1145/3065386openalex-snapshot

ImageNet classification with deep convolutional neural networks

inferredcites →https://doi.org/10.1609/aaai.v31i1.10821openalex-snapshot

Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration

inferredcites →https://doi.org/10.1609/aaai.v31i1.10924openalex-snapshot

Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning

inferredcites →https://doi.org/10.1609/aaai.v32i1.11819openalex-snapshot

Interpreting CNN Knowledge via an Explanatory Graph

inferredcites →https://doi.org/10.1609/aaai.v32i1.11833openalex-snapshot

Examining CNN Representations With Respect to Dataset Bias

inferredcites →https://doi.org/10.1631/fitee.1700808openalex-snapshot

Visual interpretability for deep learning: a survey

inferredcites →https://doi.org/10.18653/v1/p16-1228openalex-snapshot

Harnessing Deep Neural Networks with Logic Rules

inferredcites →https://doi.org/10.23915/distill.00007openalex-snapshot

Feature Visualization

inferredcites →https://doi.org/10.24963/ijcai.2017/371openalex-snapshot

Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations

inferredcites →https://doi.org/10.4230/oasics.dx.2024.27openalex-snapshot

On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)

inferredcites →https://doi.org/10.48550/arxiv.1312.6199openalex-snapshot

Intriguing properties of neural networks

inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot

Very Deep Convolutional Networks for Large-Scale Image Recognition

inferredcites →https://doi.org/10.48550/arxiv.1411.1792openalex-snapshot

How transferable are features in deep neural networks?

inferredcites →https://doi.org/10.48550/arxiv.1412.6856openalex-snapshot

Object Detectors Emerge in Deep Scene CNNs

inferredcites →https://doi.org/10.48550/arxiv.1608.08974openalex-snapshot

Towards Transparent AI Systems: Interpreting Visual Question Answering Models

inferredcites →https://doi.org/10.48550/arxiv.1703.04730openalex-snapshot

Understanding Black-box Predictions via Influence Functions

inferredcites →https://doi.org/10.48550/arxiv.1708.01783openalex-snapshot

Interactively Transferring CNN Patterns for Part Localization

inferredcites →https://doi.org/10.48550/arxiv.1806.01933openalex-snapshot

Explainable Neural Networks based on Additive Index Models

inferredcites →https://doi.org/10.57702/i9oeewf6openalex-snapshot

Caltech-UCSD Birds-200-2011 Dataset

inferredcites →https://doi.org/10.7551/mitpress/11909.003.0037openalex-snapshot

New Theory Cracks Open the Black Box of Deep Learning

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4690909openalex-extracted

inferred← citeshttps://doi.org/10.1002/advs.202204723openalex-snapshot

Semantic Interpretation for Convolutional Neural Networks: What Makes a Cat a Cat?

inferred← citeshttps://doi.org/10.1007/978-3-030-87444-5_1openalex-snapshot

Interpretable Deep Learning for Surgical Tool Management

inferred← citeshttps://doi.org/10.1007/978-3-031-70959-3_16openalex-snapshot

Recursive Division Explainability as a Factor of CNN Quality

inferred← citeshttps://doi.org/10.1007/s00521-023-08222-8openalex-snapshot

Empirical mode decomposition-based multi-scale spectral graph convolution network for abnormal electricity consumption detection

inferred← citeshttps://doi.org/10.1007/s00521-024-10437-2openalex-snapshot

Unlocking the black box: an in-depth review on interpretability, explainability, and reliability in deep learning

inferred← citeshttps://doi.org/10.1007/s11263-023-01972-xopenalex-snapshot

Interpretable Task-inspired Adaptive Filter Pruning for Neural Networks Under Multiple Constraints

inferred← citeshttps://doi.org/10.1007/s11633-022-1362-zopenalex-snapshot

Causal Reasoning Meets Visual Representation Learning: A Prospective Study

inferred← citeshttps://doi.org/10.1007/s12559-023-10238-0openalex-snapshot

Feature Analysis Network: An Interpretable Idea in Deep Learning

inferred← citeshttps://doi.org/10.1016/j.ast.2026.112417openalex-snapshot

Learning to explain and quantify for aerodynamic prediction of airfoils

inferred← citeshttps://doi.org/10.1016/j.cej.2025.171385openalex-snapshot

A deep learning-assisted interference-free and real-time decoupling multimodal sensor for tactile sensing

inferred← citeshttps://doi.org/10.1016/j.engappai.2024.108150openalex-snapshot

Learning to disentangle and fuse for fine-grained multi-modality ship image retrieval

inferred← citeshttps://doi.org/10.1016/j.engappai.2025.111009openalex-snapshot

A unified region and concept-level explainable artificial intelligence method for explainability and active learning of defect segmentation model

inferred← citeshttps://doi.org/10.1016/j.isci.2021.103581openalex-snapshot

CX-ToM: Counterfactual explanations with theory-of-mind for enhancing human trust in image recognition models

inferred← citeshttps://doi.org/10.1016/j.knosys.2024.112015openalex-snapshot

An explainable dual-mode convolutional neural network for multivariate time series classification

inferred← citeshttps://doi.org/10.1016/j.knosys.2025.114484openalex-snapshot

Feature reduction causal network (FRCN): A novel approach for analyzing coupling relationships in radar system

inferred← citeshttps://doi.org/10.1016/j.mineng.2025.109591openalex-snapshot

Machine vision-driven analysis of flotation froth properties: A comprehensive review of multidimensional feature extraction and intelligent optimization

inferred← citeshttps://doi.org/10.1016/j.neucom.2026.133349openalex-snapshot

Composite fractal scanning enhanced mamba for small object detection in remote sensing imagery

inferred← citeshttps://doi.org/10.1016/j.neucom.2026.134741openalex-snapshot

NM-Hebb: a two-phase neuromodulated Hebbian framework for convolutional neural network training

inferred← citeshttps://doi.org/10.1016/j.neunet.2024.106511openalex-snapshot

Structural prior-driven feature extraction with gradient-momentum combined optimization for convolutional neural network image classification

inferred← citeshttps://doi.org/10.1016/j.neunet.2024.106597openalex-snapshot

An attribution graph-based interpretable method for CNNs

inferred← citeshttps://doi.org/10.1016/j.patcog.2021.108421openalex-snapshot

Driving behavior explanation with multi-level fusion

inferred← citeshttps://doi.org/10.1016/j.sna.2026.118341openalex-snapshot

A triboelectric sensor with symmetrical structure applied in material identification

inferred← citeshttps://doi.org/10.1016/j.trac.2025.118141openalex-snapshot

Machine learning-assisted carbon dots synthesis and analysis: State of the art and future directions

inferred← citeshttps://doi.org/10.1021/acs.jcim.6c01426openalex-snapshot

Multiscale Interpretable Deep-Learning Framework for the Identification and Visualization of Deformation Stages in Molecular Dynamics Trajectory

inferred← citeshttps://doi.org/10.1080/10447318.2025.2526573openalex-snapshot

System Architecture Change in the Context of Double-Directional Human-AI Cognition: Rethinking and Case Studies

inferred← citeshttps://doi.org/10.1088/1361-6501/ae4d6fopenalex-snapshot

Interpretable fault diagnosis of insulated bearings in offshore wind turbines: the DRSI-former framework for complex operational regimes

inferred← citeshttps://doi.org/10.1088/1742-6596/1952/2/022009openalex-snapshot

Train the Neural Network by Abstract Images

inferred← citeshttps://doi.org/10.1109/access.2021.3054613openalex-snapshot

Explainability Metrics of Deep Convolutional Networks for Photoplethysmography Quality Assessment

inferred← citeshttps://doi.org/10.1109/access.2024.3376441openalex-snapshot

A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends

inferred← citeshttps://doi.org/10.1109/access.2024.3416370openalex-snapshot

Situation Awareness in AI-Based Technologies and Multimodal Systems: Architectures, Challenges and Applications

inferred← citeshttps://doi.org/10.1109/acpee60788.2024.10532264openalex-snapshot

Intelligent Identification Method of Sub/Super-synchronous Oscillation Parameters Based on CNN and Synchrophasor

inferred← citeshttps://doi.org/10.1109/cacml55074.2022.00089openalex-snapshot

An interpretable Neural Network and Its Application in Inferring Inter-well Connectivity

inferred← citeshttps://doi.org/10.1109/iccre57112.2023.10155583openalex-snapshot

A Feature Structure Based Interpretability Evaluation Approach for Deep Learning

inferred← citeshttps://doi.org/10.1109/iceccme64568.2025.11277681openalex-snapshot

Enhancing Reliability of Through-the-Wall Radar Imaging with Explainable Convolutional Neural Networks Techniques: Addressing Key Challenges

inferred← citeshttps://doi.org/10.1109/icoiact59844.2023.10455896openalex-snapshot

Rice Pest and Disease Classification using Transfer Learning Inception V3 with data augmentation.

inferred← citeshttps://doi.org/10.1109/iscas58744.2024.10558045openalex-snapshot

Optimized Transformer Models: ℓ′ BERT with CNN-like Pruning and Quantization

inferred← citeshttps://doi.org/10.1109/jbhi.2021.3110593openalex-snapshot

Explainable Diabetic Retinopathy Detection and Retinal Image Generation

inferred← citeshttps://doi.org/10.1109/jiot.2024.3485765openalex-snapshot

An Interpretability Optimization Method for Deep Learning Networks Based on Grad-CAM

inferred← citeshttps://doi.org/10.1109/jproc.2025.3635153openalex-snapshot

A Comprehensive Survey on Self-Interpretable Neural Networks

inferred← citeshttps://doi.org/10.1109/tase.2023.3264556openalex-snapshot

Automated Morphological Grading of Human Blastocysts From Multi-Focus Images

inferred← citeshttps://doi.org/10.1109/tcsvt.2022.3224068openalex-snapshot

An Interpretable Fusion Siamese Network for Multi-Modality Remote Sensing Ship Image Retrieval

inferred← citeshttps://doi.org/10.1109/tcsvt.2023.3314769openalex-snapshot

Transparent Embedding Space for Interpretable Image Recognition

inferred← citeshttps://doi.org/10.1109/tdsc.2024.3432169openalex-snapshot

Don't Forget Too Much: Towards Machine Unlearning on Feature Level

inferred← citeshttps://doi.org/10.1109/telepe64216.2024.00029openalex-snapshot

Research on Neural Network Structure Optimization Under the Framework of Computer Deep Learning

inferred← citeshttps://doi.org/10.1109/tgrs.2022.3162195openalex-snapshot

An Explainable Attention Network for Fine-Grained Ship Classification Using Remote-Sensing Images

inferred← citeshttps://doi.org/10.1109/tic68483.2026.11702919openalex-snapshot

AI-Reasoning and Digital Twin Architecture for Autonomous Wearable Health and Safety Systems

inferred← citeshttps://doi.org/10.1109/tifs.2022.3179951openalex-snapshot

Interpretable Local Frequency Binary Pattern (LFrBP) Based Joint Continual Learning Network for Heterogeneous Face Recognition

inferred← citeshttps://doi.org/10.1109/tii.2024.3413355openalex-snapshot

Development of an Automated Global Crash Prediction Model With Adaptive Feature Selection of Deep Neural Networks

inferred← citeshttps://doi.org/10.1109/tim.2021.3055845openalex-snapshot

A Data-Driven Interpretable Method Based on Reversely Built FIS and Its Applications in Measurement

inferred← citeshttps://doi.org/10.1109/tip.2023.3246793openalex-snapshot

Learning Patch-Channel Correspondence for Interpretable Face Forgery Detection

inferred← citeshttps://doi.org/10.1109/tnnls.2021.3071564openalex-snapshot

Whitening-Net: A Generalized Network to Diagnose the Faults Among Different Machines and Conditions

inferred← citeshttps://doi.org/10.1109/tpami.2021.3126216openalex-snapshot

2021 Index IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 43

inferred← citeshttps://doi.org/10.1109/tpami.2022.3200344openalex-snapshot

Quantifying the Knowledge in a DNN to Explain Knowledge Distillation for Classification

inferred← citeshttps://doi.org/10.1109/tpami.2024.3489597openalex-snapshot

The Decoupling Concept Bottleneck Model

inferred← citeshttps://doi.org/10.1111/exsy.70279openalex-snapshot

A Systematic Review of Explainable Convolutional Neural Networks for Image Classification

inferred← citeshttps://doi.org/10.1126/scirobotics.abm4183openalex-snapshot

In situ bidirectional human-robot value alignment

inferred← citeshttps://doi.org/10.1145/3563691openalex-snapshot

Explainable Convolutional Neural Networks: A Taxonomy, Review, and Future Directions

inferred← citeshttps://doi.org/10.21203/rs.3.rs-7043856/v1openalex-snapshot

Human-Machine Collaborative Enhanced Interpretable Distillation Model for High-Precision Online Defect Detection

inferred← citeshttps://doi.org/10.2139/ssrn.6025082openalex-snapshot

Learning to Explain and Quantify for Aerodynamic Prediction of Laminar Airfoils

inferred← citeshttps://doi.org/10.22215/etd/2022-15159openalex-snapshot

Effective Explainable Artificial Intelligence using Visual Explanations in Images

inferred← citeshttps://doi.org/10.24963/ijcai.2021/147openalex-snapshot

Learning Interpretable Concept Groups in CNNs

inferred← citeshttps://doi.org/10.25148/etd.fidc010269openalex-snapshot

An Exploration of Controlling the Content Learned by Deep Neural Networks

inferred← citeshttps://doi.org/10.31466/kfbd.1880210openalex-snapshot

Derin Öğrenme ile Duygusal Yüz İfadelerinin Tespiti: YOLOv8 ve YOLOv11 Varyantlarının Karşılaştırmalı Analizi

inferred← citeshttps://doi.org/10.3390/make8080220openalex-snapshot

From Black Box to Clarity: A Systematic Review of Explainability Methods in Deep Convolutional Neural Networks

inferred← citeshttps://doi.org/10.3390/rs17173082openalex-snapshot

RAP-Net: A Region Affinity Propagation-Guided Semantic Segmentation Network for Plateau Karst Landform Remote Sensing Imagery

inferred← citeshttps://doi.org/10.3390/s22062245openalex-snapshot

COIN: Counterfactual Image Generation for Visual Question Answering Interpretation

inferred← citeshttps://doi.org/10.3390/s23146461openalex-snapshot

The Process Analysis Method of SAR Target Recognition in Pre-Trained CNN Models

inferred← citeshttps://doi.org/10.3390/s24031015openalex-snapshot

Respecting Partial Privacy of Unstructured Data via Spectrum-Based Encoder

inferred← citeshttps://doi.org/10.48550/arxiv.2101.03613openalex-snapshot

Explainable Artificial Intelligence (XAI): An Engineering Perspective

inferred← citeshttps://doi.org/10.52547/jiaeee.20.1.67openalex-snapshot

Online learning of positive and negative prototypes with explanations based on kernel expansion

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.