Pragmatic investigations of applied deep learning in computer vision applications
Shafaei, Alireza · cIRcle (University of British Columbia) · 2020
Deep neural networks have dominated performance benchmarks on numerous machine learning tasks. These models now power the core technology of a growing list of products such as Google Search, Google Translate, Apple Siri, and even Snapchat, to mention a few. We first address two challenges in the real-world applications of deep neural networks in computer vision: data scarcity and prediction reliability. We present a new approach to data collection through synthetic data via video games that is cost-effective and can produce high-quality labelled training data on a large scale. We validate the effectiveness of synthetic data on multiple problems through cross-dataset evaluation and simple adaptive techniques. We also examine the reliability of neural network predictions in computer vision problems and show that these models are fragile on out-of-distribution test data. Motivated by statistical learning theory, we argue that it is necessary to detect out-of-distribution samples before relying on the predictions. To facilitate the development of reliable out-of-distribution sample detectors, we present a less biased evaluation framework. Using our framework, we thoroughly evaluate over ten methods from data mining, deep learning, and Bayesian methods. We show that on real-world problems, none of the evaluated methods can reliably certify a prediction. Finally, we explore the applications of deep neural networks on high-resolution portrait production pipelines. We introduce AutoPortrait, a pipeline that performs professional-grade colour-correction, portrait cropping, and portrait retouching in under two seconds. We release the first large scale professional retouching dataset.