京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第19849号 / 情博第600号 / 新制||情||104(附属図書館) / 32885 / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 河原 達也, 教授 黒橋 禎夫, 教授 鹿島 久嗣 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

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This work is aimed at exploring semi-supervised learning techniques to improve the performance of Automatic Speech Recognition systems. Semi-supervised learning takes advantage of unlabeled data in order to improve the quality of the representations extracted from the data.The proposed model is a neural network where the weights are updated by minimizing the weighted sum of a supervised and an unsupervised cost function, simultaneously. These costs are evaluated on the labeled and unlabeled portions of the data set, respectively. The combined cost is optimized through mini-batch stochastic gradient descent via standard backpropagation.The model was tested on a phone classification task on the TIMIT American English data set and on a written digit classification task on the MNIST data set. Our results show that the model outperforms a network trained with standard backpropagation on the labelled material alone. The results are also in line with state-of-the-art graph-based semi-supervised training methods. / Detta arbete syftar till att utforska halvövervakade inlärningstekniker (semi-supervised learning techniques) för att förbättra prestandan hos automatiska taligenkänningssystem.Halvövervakad maskininlärning använder sig av data ej märkt med klasstillhörighetsinformation för att förbättra kvaliteten hos den från datan extraherade representationen.Modellen som beskrivs i arbetet är ett neuralt nätverk där vikterna uppdateras genom att samtidigt minimera den viktade summan av en övervakad och en oövervakad kostnadsfunktion.Dessa kostnadsfunktioner evalueras på den märkta respektive den omärkta datamängden.De kombinerade kostnadsfunktionerna optimeras genom gradient descent med hjälp av traditionell backpropagation.Modellen har evaluerats genom en fonklassificeringsuppgift på datamängden TIMIT American English, samt en sifferklassificeringsuppgift på datamängden MNIST.Resultaten visar att modellen presterar bättre än ett nätverk tränat med backpropagation på endast märkt data.Resultaten är även konkurrenskraftiga med rådande state of the art, grafbaserade halvövervakade inlärningsmetoder.

This project focuses on semi-supervised human activity recognition (HAR) tasks, in which the inputs are partly labeled time series data acquired from sensors such as accelerometer data, and the outputs are predefined human activities. Most state-of-the-art existing work in HAR area is supervised now, which relies on fully labeled datasets. Since the cost to label the collective instances increases fast with the increasing scale of data, semi-supervised methods are now widely required. This report proposed two semi-supervised methods and then investigated how well they perform on a partly labeled dataset, comparing to the state-of-the-art supervised method. One of these methods is designed based on the state-of-the-art supervised method, Deep-ConvLSTM, together with the semi-supervised learning concepts, self-training. Another one is modified based on a semi-supervised deep learning method, LSTM initialized by seq2seq autoencoder, which is firstly introduced for natural language processing. According to the experiments on a published dataset (Opportunity Activity Recognition dataset), both of these semi-supervised methods have better performance than the state-of-the-art supervised methods. / Detta projekt fokuserar på halvövervakad Human Activity Recognition (HAR), där indata delvis är märkta tidsseriedata från sensorer som t.ex. accelerometrar, och utdata är fördefinierade mänskliga aktiviteter. De främsta arbetena inom HAR-området använder numera övervakade metoder, vilka bygger på fullt märkta dataset. Eftersom kostnaden för att märka de samlade instanserna ökar snabbt med den ökade omfattningen av data, föredras numera ofta halvövervakade metoder. I denna rapport föreslås två halvövervakade metoder och det undersöks hur bra de presterar på ett delvis märkt dataset jämfört med den moderna övervakade metoden. En av dessa metoder utformas baserat på en högkvalitativ övervakad metod, DeepConvLSTM, kombinerad med självutbildning. En annan metod baseras på en halvövervakad djupinlärningsmetod, LSTM, initierad av seq2seq autoencoder, som först införs för behandling av naturligt språk. Enligt experimenten på ett publicerat dataset (Opportunity Activity Recognition dataset) har båda dessa metoder bättre prestanda än de toppmoderna övervakade metoderna.

For many real-world applications, labeled data can be costly to obtain. Semi-supervised learning methods make use of substantially available unlabeled data along with few labeled samples. Most of the latest work on semi-supervised learning for image classification show performance on standard machine learning datasets like MNIST, SVHN, etc. In this work, we propose a convolutional adversarial autoencoder architecture for real-world data. We demonstrate the application of this architecture for semi-supervised object recognition. We show that our approach can learn from limited labeled data and outperform fully-supervised CNN baseline method by about 4% on real-world datasets. We also achieve competitive performance on the MNIST dataset compared to state-of-the-art semi-supervised learning techniques. To spur research in this direction, we compiled two real-world datasets: Internet (WIS) dataset and Real-world (RW) dataset which consists of more than 20K labeled samples each, comprising of small household objects belonging to ten classes. We also show a possible application of this method for online learning in robotics. / I de flesta verklighetsbaserade tillämpningar kan det vara kostsamt att erhålla märkt data. Inlärningsmetoder som är semi-övervakade använder sig oftast i stor utsträckning av omärkt data med stöd av en liten mängd märkt data. Mycket av det senaste arbetet inom semiövervakade inlärningsmetoder för bildklassificering visar prestanda på standardiserad maskininlärning så som MNIST, SVHN, och så vidare. I det här arbetet föreslår vi en convolutional adversarial autoencoder arkitektur för verklighetsbaserad data. Vi demonstrerar tillämpningen av denna arkitektur för semi-övervakad objektidentifiering och visar att vårt tillvägagångssätt kan lära sig av ett begränsat antal märkt data. Därmed överträffar vi den fullt övervakade CNN-baslinjemetoden med ca. 4% på verklighetsbaserade datauppsättningar. Vi uppnår även konkurrenskraftig prestanda på MNIST datauppsättningen jämfört med moderna semi-övervakade inlärningsmetoder. För att stimulera forskningen i den här riktningen, samlade vi två verklighetsbaserade datauppsättningar: Internet (WIS) och Real-world (RW) datauppsättningar, som består av mer än 20 000 märkta prov vardera, som utgörs av små hushållsobjekt tillhörandes tio klasser. Vi visar också en möjlig tillämpning av den här metoden för online-inlärning i robotik.

This thesis addresses the crucial problem of knowledge transfer and retention in deep neural networks. The ability to transfer knowledge from previously learned tasks and retain it for future use is essential for machine learning models to continually adapt to new tasks and improve their overall performance. In principle, knowledge can be transferred between any type of task, but we believe it to be particularly challenging in the field of computer vision, where the size and diversity of visual data often result in high compute requirements and the need for large, complex models. Hence, we analyze transfer and retention learning between unsupervised and supervised visual tasks, which form the main focus of this thesis. We categorize our efforts into several knowledge transfer and retention paradigms, and we tackle them with several contributions for the scientific community. The thesis proposes settings and methods based on knowledge distillation and self-supervised learning techniques. In particular, we devise two novel continual learning settings and seven new methods for knowledge transfer and retention, setting new state-of-the-art in a wide range of tasks. In conclusion, this thesis provides a valuable contribution to the field of computer vision and machine learning and sets a foundation for future work in this area.

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Face recognition is a challenging problem in computer vision. Difficulties such as slight differences between similar faces of different people, changes in facial expressions, light and illumination condition, and pose variations add extra complications to the face recognition research. Many algorithms are devoted to solving the face recognition problem, among which the family of nonnegative matrix factorization (NMF) algorithms has been widely used as a compact data representation method. Different versions of NMF have been proposed. Wang et al. proposed the graph-based semi-supervised nonnegative learning (S2N2L) algorithm that uses labeled data in constructing intrinsic and penalty graph to enforce separability of labeled data, which leads to a greater discriminating power. Moreover the geometrical structure of labeled and unlabeled data is preserved through using the smoothness assumption by creating a similarity graph that conserves the neighboring information for all labeled and unlabeled data. However, S2N2L is sensitive to light changes, illumination, and partial occlusion. In this thesis, we propose a Semi-Supervised Half-Quadratic NMF (SSHQNMF) algorithm that combines the benefits of S2N2L and the robust NMF by the half- quadratic minimization (HQNMF) algorithm.Our algorithm improves upon the S2N2L algorithm by replacing the Frobenius norm with a robust M-Estimator loss function. A multiplicative update solution for our SSHQNMF algorithmis driven using the half- 4 quadratic (HQ) theory. Extensive experiments on ORL, Yale-A and a subset of the PIE data sets for nine M-estimator loss functions for both SSHQNMF and HQNMF algorithms are investigated, and compared with several state-of-the-art supervised and unsupervised algorithms, along with the original S2N2L algorithm in the context of classification, clustering, and robustness against partial occlusion. The proposed algorithm outperformed the other algorithms. Furthermore, SSHQNMF with Maximum Correntropy (MC) loss function obtained the best results for most test cases.