# Deep Learning in the Wild

arXiv: https://arxiv.org/abs/1807.04950 · HTML: https://mlsanity.com/paper/1807.04950/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2018.

## Built on by (most cited first, 12 of 12)

- [Scalable agent alignment via reward modeling: a research direction](https://mlsanity.com/paper/1811.07871/) (2018)
- [A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions](https://mlsanity.com/paper/2307.05638/) (2023)
- [Is it enough to optimize CNN architectures on ImageNet?](https://mlsanity.com/paper/2103.09108/) (2021)
- [Efficient Rotation Invariance in Deep Neural Networks through Artificial Mental Rotation](https://mlsanity.com/paper/2311.08525/) (2023)
- [Synthetic Tabular Data Detection In the Wild](https://mlsanity.com/paper/2503.01937/) (2025)
- [A Theory of Natural Intelligence](https://mlsanity.com/paper/2205.00002/) (2022)
- [Uncertainty Guarantees on Automated Precision Weeding using Conformal Prediction](https://mlsanity.com/paper/2501.07185/) (2025)
- [Utilizing Active Machine Learning for Quality Assurance: A Case Study of Virtual Car Renderings in the Automotive Industry](https://mlsanity.com/paper/2110.09023/) (2021)
- [Proceedings of the 1st International Workshop on Reading Music Systems](https://mlsanity.com/paper/2301.10062/) (2022)
- [Agave crop segmentation and maturity classification with deep learning data-centric strategies using very high-resolution satellite imagery](https://mlsanity.com/paper/2303.11564/) (2023)
- [Cross-table Synthetic Tabular Data Detection](https://mlsanity.com/paper/2412.13227/) (2024)
- [A document is worth a structured record: Principled inductive bias design for document recognition](https://mlsanity.com/paper/2507.08458/) (2025)

## Abstract (arXiv)

Deep learning with neural networks is applied by an increasing number of people outside of classic research environments, due to the vast success of the methodology on a wide range of machine perception tasks. While this interest is fueled by beautiful success stories, practical work in deep learning on novel tasks without existing baselines remains challenging. This paper explores the specific challenges arising in the realm of real world tasks, based on case studies from research & development in conjunction with industry, and extracts lessons learned from them. It thus fills a gap between the publication of latest algorithmic and methodical developments, and the usually omitted nitty-gritty of how to make them work. Specifically, we give insight into deep learning projects on face matching, print media monitoring, industrial quality control, music scanning, strategy game playing, and automated machine learning, thereby providing best practices for deep learning in practice.