# Improving Deep Learning Models via Constraint-Based Domain Knowledge: a Brief Survey

arXiv: https://arxiv.org/abs/2005.10691 · HTML: https://mlsanity.com/paper/2005.10691/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2020.

## Built on by (most cited first, 16 of 16)

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- [Causal Discovery and Knowledge Injection for Contestable Neural Networks (with Appendices)](https://mlsanity.com/paper/2205.09787/) (2022)
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- [Integrating Symbolic Reasoning into Neural Generative Models for Design Generation](https://mlsanity.com/paper/2310.09383/) (2023)
- [Guiding Video Prediction with Explicit Procedural Knowledge](https://mlsanity.com/paper/2406.18220/) (2024)
- [On Aligning Prediction Models with Clinical Experiential Learning: A Prostate Cancer Case Study](https://mlsanity.com/paper/2509.04053/) (2025)
- [A Clustering-aided Ensemble Method for Predicting Ridesourcing Demand in Chicago](https://mlsanity.com/paper/2109.03433/) (2021)
- [Hyperplane bounds for neural feature mappings](https://mlsanity.com/paper/2201.05799/) (2022)
- [Knowledge-Integrated Informed AI for National Security](https://mlsanity.com/paper/2202.03188/) (2022)
- [Informed Learning by Wide Neural Networks: Convergence, Generalization and Sampling Complexity](https://mlsanity.com/paper/2207.00751/) (2022)
- [CorrLoss: Integrating Co-Occurrence Domain Knowledge for Affect Recognition](https://mlsanity.com/paper/2210.17233/) (2022)
- [Informative regularization for a multi-layer perceptron RR Lyrae classifier under data shift](https://mlsanity.com/paper/2303.06544/) (2023)
- [Great GATsBi: Hybrid, Multimodal, Trajectory Forecasting for Bicycles using Anticipation Mechanism](https://mlsanity.com/paper/2508.14523/) (2025)

## Abstract (arXiv)

Deep Learning (DL) models proved themselves to perform extremely well on a wide variety of learning tasks, as they can learn useful patterns from large data sets. However, purely data-driven models might struggle when very difficult functions need to be learned or when there is not enough available training data. Fortunately, in many domains prior information can be retrieved and used to boost the performance of DL models. This paper presents a first survey of the approaches devised to integrate domain knowledge, expressed in the form of constraints, in DL learning models to improve their performance, in particular targeting deep neural networks. We identify five (non-mutually exclusive) categories that encompass the main approaches to inject domain knowledge: 1) acting on the features space, 2) modifications to the hypothesis space, 3) data augmentation, 4) regularization schemes, 5) constrained learning.