mlsanityData to 7 Oct 2026

Towards Fully Interpretable Deep Neural Networks: Are We There Yet?

arXiv 2106.13164 ↗First posted 24 Jun 2021cs.LG cs.CVcited by 1

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Abstract (arXiv)

Despite the remarkable performance, Deep Neural Networks (DNNs) behave as black-boxes hindering user trust in Artificial Intelligence (AI) systems. Research on opening black-box DNN can be broadly categorized into post-hoc methods and inherently interpretable DNNs. While many surveys have been conducted on post-hoc interpretation methods, little effort is devoted to inherently interpretable DNNs. This paper provides a review of existing methods to develop DNNs with intrinsic interpretability, with a focus on Convolutional Neural Networks (CNNs). The aim is to understand the current progress towards fully interpretable DNNs that can cater to different interpretation requirements. Finally, we identify gaps in current work and suggest potential research directions.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 27 of its 43 references that are in the map. First posted on arXiv 24 Jun 2021.

Builds on

27 of 43 references.

References · 27

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Densely Connected Convolutional Networkscited by 6,691
  3. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  4. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  5. 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
  6. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,801
  7. 2015Learning Deep Features for Discriminative Localizationcited by 2,430
  8. 2018Deep contextualized word representationscited by 2,285
  9. 2013Visualizing and Understanding Convolutional Networkscited by 2,278
  10. 2017Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)cited by 1,309
  11. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,182
  12. 2019EfficientDet: Scalable and Efficient Object Detectioncited by 1,018
15 more
  1. 2018This Looks Like That: Deep Learning for Interpretable Image Recognitioncited by 731
  2. 2017Network Dissection: Quantifying Interpretability of Deep Visual Representationscited by 703
  3. 2018Towards Robust Interpretability with Self-Explaining Neural Networkscited by 505
  4. 2016From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classificationcited by 337
  5. 2017Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictionscited by 234
  6. 2018Multimodal Explanations: Justifying Decisions and Pointing to the Evidencecited by 179
  7. 2017Interpretable Convolutional Neural Networkscited by 161
  8. 2016Generating Visual Explanationscited by 156
  9. 2014Part-based R-CNNs for Fine-grained Category Detectioncited by 107
  10. 2020Learning Attentive Pairwise Interaction for Fine-Grained Classificationcited by 84
  11. 2020Interpretable and Accurate Fine-grained Recognition via Region Groupingcited by 75
  12. 2020Training Interpretable Convolutional Neural Networks by Differentiating Class-specific Filterscited by 26
  13. 2020Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Functioncited by 23
  14. 2018Explaining Neural Networks Semantically and Quantitativelycited by 20
  15. 2021Comprehensible Convolutional Neural Networks via Guided Concept Learningcited by 11

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