Deep Learning in the Wild

By Thilo Stadelmann, Mohammadreza Amirian, Ismail Arabaci, Marek Arnold, Gilbert François Duivesteijn, Ismail Elezi, Melanie Geiger, Stefan Lörwald, Benjamin Bruno Meier, Katharina Rombach and 1 more. 2018

45 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Tom Everitt
2. Jan Leike
3. Shane Legg
4. David Krueger
5. Miljan Martic
6. Wei-Wei Tu
7. Quanming Yao
8. Yang Yu

In Computer Vision and Pattern Recognition

1. Quanming Yao
2. Hugo Jair Escalante
3. Isabelle M Guyon
4. Qiang Yang
5. Yang Yu
6. David Krueger
7. Tom Everitt
8. Mohammadreza Amirian

Citations in the corpus

• Scalable agent alignment via reward modeling: a research direction (2018, background): “Yet there are still many challenges to be solved in order to make deep RL useful in the real world (Stadelmann et al., 2018; Irpan, 2018; Marcus, 2018); in particular, we need algorithms that can learn to perform complex tasks as intended in the absence of a hand-engineered reward function.”
• Automated Machine Learning: From Principles to Practices (2018, methodology): “Tables 9 present the experimental results of the NAS methods.”
• Automated Machine Learning in Practice: State of the Art and Recent Results (2019, methodology): “Auto-sklearn [17]: We used the algorithm explained in Section III to find the best pipeline within the time-budget computed for training 100 models by DSM. Auto-sklearn is slow in start [17]; however, it eventually reaches a solution very close to the optimum.”