Self-supervised learning techniques for monitoring industrial spaces

Dissertação de mestrado em Matemática e ComputaçãoEste documento é uma Dissertação de Mestrado com o título ”Self-Supervised Learning Techniques for Monitoring Industrial Spaces”e foi realizada e ambiente empresarial na empresa Neadvance - Machine Vision S.A. em conjunto com a Universidade do Minho. Esta dissertação surge de um grande projeto que consiste no desenvolvimento de uma plataforma de monitorização de operações específicas num espaço industrial, denominada SMARTICS (Plataforma tecnoló gica para monitorização inteligente de espaços industriais abertos). Este projeto continha uma componente de investigação para explorar um paradigma de aprendizagem diferente e os seus métodos - self-supervised learning, que foi o foco e principal contributo deste trabalho. O supervised learning atingiu um limite, pois exige anotações caras e dispendiosas. Em problemas reais, como em espaços industriais nem sempre é possível adquirir um grande número de imagens. O self-supervised learning ajuda nesses problemas, ex traindo informações dos próprios dados e alcançando bom desempenho em conjuntos de dados de grande escala. Este trabalho fornece uma revisão geral da literatura sobre a estrutura de self-supervised learning e alguns métodos. Também aplica um método para resolver uma tarefa de classificação para se assemelhar a um problema em um espaço industrial.This document is a Master’s Thesis with the title ”Self-Supervised Learning Techniques for Monitoring Industrial Spaces” and was carried out in a business environment at Neadvance - Machine Vision S.A. together with the University of Minho. This dissertation arises from a major project that consists of developing a platform to monitor specific operations in an industrial space, named SMARTICS (Plataforma tecnológica para monitorização inteligente de espaços industriais abertos). This project contained a research component to explore a different learning paradigm and its methods - self-supervised learning, which was the focus and main contribution of this work. Supervised learning has reached a bottleneck as they require expensive and time-consuming annotations. In real problems, such as in industrial spaces it is not always possible to require a large number of images. Self-supervised learning helps these issues by extracting information from the data itself and has achieved good performance in large-scale datasets. This work provides a comprehensive literature review of the self supervised learning framework and some methods. It also applies a method to solve a classification task to resemble a problem in an industrial space and evaluate its performance

Rethinking auto-colourisation of natural Images in the context of deep learning

Auto-colourisation is the ill-posed problem of creating a plausible full-colour image from a grey-scale prior. The current state of the art utilises image-to-image Generative Adversarial Networks (GANs). The standard method for training colourisation is reformulating RGB images into a luminance prior and two-channel chrominance supervisory signal. However, progress in auto-colourisation is inherently limited by multiple prerequisite dilemmas, where unsolved problems are mutual prerequisites. This thesis advances the field of colourisation on three fronts: architecture, measures, and data. Changes are recommended to common GAN colourisation architectures. Firstly, removing batch normalisation from the discriminator to allow the discriminator to learn the primary statistics of plausible colour images. Secondly, eliminating the direct L1 loss on the generator as L1 will limit the discovery of the plausible colour manifold. The lack of an objective measure of plausible colourisation necessitates resource-intensive human evaluation and repurposed objective measures from other fields. There is no consensus on the best objective measure due to a knowledge gap regarding how well objective measures model the mean human opinion of plausible colourisation. An extensible data set of human-evaluated colourisations, the Human Evaluated Colourisation Dataset (HECD) is presented. The results from this dataset are compared to the commonly-used objective measures and uncover a poor correlation between the objective measures and mean human opinion. The HECD can assess the future appropriateness of proposed objective measures. An interactive tool supplied with the HECD allows for a first exploration of the space of plausible colourisation. Finally, it will be shown that the luminance channel is not representative of the legacy black-and-white images that will be presented to models when deployed; This leads to out-of-distribution errors in all three channels of the final colour image. A novel technique is proposed to simulate priors that match any black-and-white media for which the spectral response is known