mlsanityData to 14 Oct 2026

C-SENN: Contrastive Self-Explaining Neural Network

arXiv 2206.09575 ↗First posted 20 Jun 2022cs.CV cs.AI cs.LGcited by 9

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Abstract (arXiv)

In this study, we use a self-explaining neural network (SENN), which learns unsupervised concepts, to acquire concepts that are easy for people to understand automatically. In concept learning, the hidden layer retains verbalizable features relevant to the output, which is crucial when adapting to real-world environments where explanations are required. However, it is known that the interpretability of concepts output by SENN is reduced in general settings, such as autonomous driving scenarios. Thus, this study combines contrastive learning with concept learning to improve the readability of concepts and the accuracy of tasks. We call this model Contrastive Self-Explaining Neural Network (C-SENN).

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 14 of its 18 references that are in the map. First posted on arXiv 20 Jun 2022.

Builds on

14 of 18 references.

References · 14

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  2. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  3. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
  4. 2020Exploring Simple Siamese Representation Learningcited by 2,866
  5. 2020Supervised Contrastive Learningcited by 2,829
  6. 2021Barlow Twins: Self-Supervised Learning via Redundancy Reductioncited by 1,838
  7. 2019Dream to Control: Learning Behaviors by Latent Imaginationcited by 1,653
  8. 2018BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learningcited by 1,288
  9. 2021VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learningcited by 1,057
  10. 2020Concept Bottleneck Modelscited by 1,032
  11. 2018Towards Robust Interpretability with Self-Explaining Neural Networkscited by 505
  12. 2020Understanding the Behaviour of Contrastive Losscited by 398
2 more
  1. 2020Explainable Object-induced Action Decision for Autonomous Vehiclescited by 88
  2. 2022Concept Bottleneck Model with Additional Unsupervised Conceptscited by 79

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 4; 2026: 1.

9 papers.

  1. 2023Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Sciencecited by 27
  2. 2025If Concept Bottlenecks are the Question, are Foundation Models the Answer?cited by 15
  3. 2025Interpretable Machine Learning in Physics: A Reviewcited by 14
  4. 2023Concept Distillation: Leveraging Human-Centered Explanations for Model Improvementcited by 10
  5. 2024A survey on Concept-based Approaches For Model Improvementcited by 8
  6. 2025A Comprehensive Survey on the Risks and Limitations of Concept-based Modelscited by 4
  7. 2024Advancing Ante-Hoc Explainable Models through Generative Adversarial Networkscited by 2
  8. 2025Self-Explaining Reinforcement Learning for Mobile Network Resource Allocationcited by 0
  9. 2026Interpretable but Fragile? Robustness of Concept Bottlenecks under Geometric-Semantic Perturbationscited by 0