Beyond ImageNet: Deep Learning in Industrial Practice

Abstract

Deep learning (DL) methods have gained considerable attention since 2014. In this chapter we briefly review the state of the art in DL and then give several examples of applications from diverse areas of application. We will focus on convolutional neural networks (CNNs), which have since the seminal work of Krizhevsky et al. (2012) revolutionized image classification and even started surpassing human performance on some benchmark data sets (Ciresan et al., 2012a, He et al., 2015a). While deep neural networks have become popular primarily for image classification tasks, they can also be successfully applied to other areas and problems with some local structure in the data. We will first present a classical application of CNNs on image-like data, in particular, phenotype classification of cells based on their morphology, and then extend the task to clustering voices based on their spectrograms. Next, we will describe DL applications to semantic segmentation of newspaper pages into their corresponding articles based on clues in the pixels, and outlier detection in a predictive maintenance setting. We conclude by giving advice on how to work with DL having limited resources (e.g., training data).

Deep Learning in the Wild

“…For this form of article-based information retrieval, it is necessary to segment tens of thousands of newspaper pages into articles daily. We successfully developed neural network-based models to learn how to segment pages into their constituting articles and described their details elsewhere [57,35] (see example results in Figure 3a-b). In this section, we present challenges faced and learnings gained from integrating a respective model into a production environment with strict performance and reliability requirements.…”

“…Adding to this fact, with a notable exception [20], the field lacks authoritative and detailed textbooks by leading representatives. Learners are thus left with preprints [37,57], cookbooks [44], code 3 and older gems [29,28,58] to find much needed practical advice.…”

A Survey of Un-, Weakly-, and Semi-Supervised Learning Methods for Noisy, Missing and Partial Labels in Industrial Vision Applications

“…Utilizing machine learning in an industrial application poses additional challenges compared to research lab environments [1], [2], e.g., in the form of data quality and data quantity issues [3]. "Garbage in, Garbage out" is an often stressed dictum in machine learning -even more so in industrial applications, where data samples and labels collection is difficult and costly [4].…”

Two to Trust: AutoML for Safe Modelling and Interpretable Deep Learning for Robustness

“…The recent success of machine learning (ML) and deep learning (DL) has triggered enormous interest in practical applications of these algorithms in many organizations [17,18]. The emergence of automated ML (AutoML), which includes automated DL (AutoDL), further expands the horizons of such machine learning applications for non-experts and broadens the feasibility of exploring larger search spaces during development.…”