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Pragmatic investigations of applied deep learning in computer vision applications Shafaei, Alireza
Abstract
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.
Item Metadata
| Title |
Pragmatic investigations of applied deep learning in computer vision applications
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| Creator | |
| Publisher |
University of British Columbia
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| Date Issued |
2020
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| Description |
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.
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| Genre | |
| Type | |
| Language |
eng
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| Date Available |
2020-12-17
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| Provider |
Vancouver : University of British Columbia Library
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| Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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| DOI |
10.14288/1.0395340
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| URI | |
| Degree | |
| Program | |
| Affiliation | |
| Degree Grantor |
University of British Columbia
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| Graduation Date |
2021-05
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| Campus | |
| Scholarly Level |
Graduate
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| Rights URI | |
| Aggregated Source Repository |
DSpace
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Rights
Attribution-NonCommercial-NoDerivatives 4.0 International