# Towards Explainable Deep Neural Networks (xDNN)

arXiv: https://arxiv.org/abs/1912.02523 · HTML: https://mlsanity.com/paper/1912.02523/

## Key facts

- Cited by 61 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2019.

## Built on by (most cited first, 30 of 61)

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- [Interpretability of Machine Learning: Recent Advances and Future Prospects](https://mlsanity.com/paper/2305.00537/) (2023)
- [Explainable AI (XAI) in Image Segmentation in Medicine, Industry, and Beyond: A Survey](https://mlsanity.com/paper/2405.01636/) (2024)
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- [A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures](https://mlsanity.com/paper/2404.00673/) (2024)
- [Towards Quantitative Evaluation of Explainable AI Methods for Deepfake Detection](https://mlsanity.com/paper/2404.18649/) (2024)
- [A Comprehensive Review of Trends, Applications and Challenges In Out-of-Distribution Detection](https://mlsanity.com/paper/2209.12935/) (2022)
- [Multimodal and Explainable Internet Meme Classification](https://mlsanity.com/paper/2212.05612/) (2022)
- [Less is More: The Influence of Pruning on the Explainability of CNNs](https://mlsanity.com/paper/2302.08878/) (2023)
- [Explainable AI applications in the Medical Domain: a systematic review](https://mlsanity.com/paper/2308.05411/) (2023)
- [Image Translation as Diffusion Visual Programmers](https://mlsanity.com/paper/2401.09742/) (2024)
- [CONFINE: Conformal Prediction for Interpretable Neural Networks](https://mlsanity.com/paper/2406.00539/) (2024)
- [Efficient Deep Learning Infrastructures for Embedded Computing Systems: A Comprehensive Survey and Future Envision](https://mlsanity.com/paper/2411.01431/) (2024)
- [Scientometric Review of Artificial Intelligence for Operations & Maintenance of Wind Turbines: The Past, Present and Future](https://mlsanity.com/paper/2204.02360/) (2022)
- [A multi-level interpretable sleep stage scoring system by infusing experts' knowledge into a deep network architecture](https://mlsanity.com/paper/2207.04585/) (2022)
- [An Interpretable Deep Semantic Segmentation Method for Earth Observation](https://mlsanity.com/paper/2210.12820/) (2022)
- [Self-Supervised Representation Learning for Adversarial Attack Detection](https://mlsanity.com/paper/2407.04382/) (2024)
- [Sanity checks and improvements for patch visualisation in prototype-based image classification](https://mlsanity.com/paper/2302.08508/) (2023)
- [Improving Prototypical Visual Explanations with Reward Reweighing, Reselection, and Retraining](https://mlsanity.com/paper/2307.03887/) (2023)
- [CACTUS: a Comprehensive Abstraction and Classification Tool for Uncovering Structures](https://mlsanity.com/paper/2308.12031/) (2023)
- [Robust Text Classification: Analyzing Prototype-Based Networks](https://mlsanity.com/paper/2311.06647/) (2023)
- [Towards interpretable-by-design deep learning algorithms](https://mlsanity.com/paper/2311.11396/) (2023)
- [Towards Privacy-preserving Explanations in Medical Image Analysis](https://mlsanity.com/paper/2107.09652/) (2021)

## Abstract (arXiv)

In this paper, we propose an elegant solution that is directly addressing the bottlenecks of the traditional deep learning approaches and offers a clearly explainable internal architecture that can outperform the existing methods, requires very little computational resources (no need for GPUs) and short training times (in the order of seconds). The proposed approach, xDNN is using prototypes. Prototypes are actual training data samples (images), which are local peaks of the empirical data distribution called typicality as well as of the data density. This generative model is identified in a closed form and equates to the pdf but is derived automatically and entirely from the training data with no user- or problem-specific thresholds, parameters or intervention. The proposed xDNN offers a new deep learning architecture that combines reasoning and learning in a synergy. It is non-iterative and non-parametric, which explains its efficiency in terms of time and computational resources. From the user perspective, the proposed approach is clearly understandable to human users. We tested it on some well-known benchmark data sets such as iRoads and Caltech-256. xDNN outperforms the other methods including deep learning in terms of accuracy, time to train and offers a clearly explainable classifier. In fact, the result on the very hard Caltech-256 problem (which has 257 classes) represents a world record.