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Most semantic segmentation methods based on Convolutional Neural Networks (CNNs) rely on supervised pixel-level labelling, but because pixel-level labelling is time-consuming and laborious, synthetic images are generated by software, and their label information is already embedded inside the data; therefore, labelling can be done automatically. This advantage makes synthetic datasets widely used in training deep learning models for real-world cases. Still, compared to supervised learning with real-world labelled images, the accuracy of the models trained using synthetic datasets is not high when applied to real-world data. So, researchers have turned their interest to Unsupervised Domain Adaptation (UDA), which is mainly used to transfer knowledge learned from one domain to another. That is why we can use synthetic data to train the model. Then, the model can use what it learned to deal with real-world problems. UDA is an essential part of transfer learning. It aims to make two domain feature distributions as close as possible. In other words, UDA is mainly used to migrate the learned knowledge from one domain to another, so the knowledge and distribution learned from the source domain feature space can be migrated to the target space to improve the prediction accuracy of the target domain. However, compared with the traditional supervised learning model, the accuracy of UDA is not high when the trained UDA is used for scene segmentation of real images. The reason for the low accuracy of UDA is that the domain gap between the source and target domains is too large. The image distribution information learned by the model from the source domain cannot be applied to the target domain, which limits the development of UDA. Therefore we propose a new UDA model called TwinLossGAN, which will reduce the domain gap in two steps. The first step is to mix images from the source and target domains. The purpose is to allow the model to learn the features of images from both domains well. Mixing is performed by selecting a synthetic image on the source domain and then selecting a real-world image on the target domain. The two selected images are input to the segmenter to obtain semantic segmentation results separately. Then, the segmentation results are fed into the mixing module. The mixing model uses the ClassMix method to copy and paste some segmented objects from one image into another using segmented masks. Additionally, it generates inter-domain composite images and the corresponding pseudo-label. Then, in the second step, we modify a Generative Adversarial Network (GAN) to reduce the gap between domains further. The original GAN network has two main parts: generator and discriminator. In our proposed TwinLossGAN, the generator performs semantic segmentation on the source domain images and the target domain images separately. Segmentations are trained in parallel. The source domain synthetic images are segmented, and the loss is computed using synthetic labels. At the same time, the generated inter-domain composite images are fed to the segmentation module. The module compares its semantic segmentation results with the pseudo-label and calculates the loss. These calculated twin losses are used as generator loss for the GAN cycle for iterations. The GAN discriminator examines whether the semantic segmentation results originate from the source or target domain. The premise was that we retrieved data from GTA5 and SYNTHIA as the source domain data and images from CityScapes as the target domain data. The result was that the accuracy indicated by the TwinLossGAN that we proposed was much higher than the base UDA models.

In Sweden, women of age between of 40 and 74 go through regular screening of their breasts every 18-24 months. The screening mainly involves obtaining a mammogram and having radiologists analyze them to detect any sign of breast cancer. However reading a mammography image requires experienced radiologist, and the lack of radiologist reduces the hospital's operating efficiency. What's more, mammography from different facilities increases the difficulty of diagnosis. Our work proposed a deep learning segmentation system which could adapt to mammography from various facilities and locate the position of the tumor. We train and test our method on two public mammography datasets and do several experiments to find the best parameter setting for our system. The test segmentation results suggest that our system could play as an auxiliary diagnosis tool for breast cancer diagnosis and improves diagnostic accuracy and efficiency. / I Sverige går kvinnor i åldrarna mellan 40 och 74 igenom regelbunden screening av sina bröst med 18-24 månaders mellanrum. Screeningen innbär huvudsakligen att ta mammogram och att låta radiologer analysera dem för att upptäcka tecken på bröstcancer. Emellertid krävs det en erfaren radiolog för att tyda en mammografibild, och bristen på radiologer reducerar sjukhusets operativa effektivitet. Dessutom, att mammografin kommer från olika anläggningar ökar svårigheten att diagnostisera. Vårt arbete föreslår ett djuplärande segmenteringssystem som kan anpassa sig till mammografi från olika anläggningar och lokalisera tumörens position. Vi tränar och testar vår metod på två offentliga mammografidataset och gör flera experiment för att hitta den bästa parameterinställningen för vårt system. Testsegmenteringsresultaten tyder på att vårt system kan fungera som ett hjälpdiagnosverktyg vid diagnos av bröstcancer och förbättra diagnostisk noggrannhet och effektivitet.

<p dir="ltr">Recent advances in Artificial Intelligence (AI) have been markedly accelerated by the convergence of advances in Machine Learning (ML) and the exponential growth in computational power. Within this dynamic landscape, the concept of Domain Adaptation (DA) is dedicated to the seamless transference of knowledge across domains characterized by disparate data distributions. This thesis ventures into the challenging and nuanced terrain of Online Unsupervised Domain Adaptation (OUDA), where the unlabeled data stream arrives from the target domain incrementally and gradually diverges from the source domain. This thesis presents two innovative and complementary approaches -- a manifold-based approach and a time-domain-based approach -- to effectively tackle the intricate OUDA challenges.</p><p dir="ltr">The manifold-based approach seeks to address this gap by incorporating the domain alignment process in an incremental computation manner, and this novel technique leverages the computation of transformation matrices, based on the projection of both source and target data onto the Grassmann manifold. This projection aligns both domains by incrementally minimizing their dissimilarities, effectively ameliorating the divergence between the source and target data. This manifold-based approach capitalizes on the cumulative temporal information within the data stream, utilizing the Incremental Computation of Mean-Subspace (ICMS) technique. This technique efficiently computes the average subspace of target subspaces on the Grassmann manifold, adeptly capturing the evolving dynamics of the data distribution. The alignment process is further fortified by integrating the flow of target subspaces on the manifold. As the target data stream unfolds over time, this approach incorporates this information, yielding robust and adaptive transformation matrices. In addition, the efficient computation of the mean-subspace, closely aligned with the Karcher mean, attests to the computational feasibility of the manifold-based approach, thus, enabling real-time feedback computations for the OUDA problem.</p><p dir="ltr">The time-domain-based approach utilizes the cluster-wise information and its flow information from each time-step to accurately predict target labels in the incoming target data, propagate consistently the class labels to future incoming target data, and efficiently utilize the predicted labels in the target data together with the source data to incrementally update the learning model in a supervised-learning scenario. This process effectively transforms the OUDA problem into a supervised-learning scenario. We leverage a neural-network-based model to align target features, cluster them class-wise and extend them linearly from the origin of the latent space as the time-step progresses. This alignment process enables accurate predictions and target label propagation based on the trajectories of the target features. We achieve target label propagation through the novel Flow-based Hierarchical Optimal Transport (FHOT) method, which considers element-wise, cluster-wise, and distribution-wise correspondences of adjacent target features. The learning model is continuously updated with incoming target data and their predicted labels.</p><p dir="ltr">To comprehensively assess the impact and contributions of these two approaches to the OUDA problem, we conducted extensive experiments across diverse datasets. Our analysis covered each stage of the manifold-based approach, comparing its performance with prior methods in terms of classification accuracy and computational efficiency. The time-domain-based approach was validated through linear feature alignment in the latent space, resulting in accurate label predictions. Notably, the flow-based hierarchical optimal transport technique substantially enhanced classification accuracy, particularly with increasing time-steps. Furthermore, learning model updates using target data and predicted labels significantly improved classification accuracy.</p>

In this thesis, I explore the problem of low-resource domain adaptation for jihadi discourse. Due to the limited availability of annotated parallel data, developing accurate and effective models in this domain poses a challenging task. To address this issue, I propose a method that leverages a small in-domain manually created corpus and a synthetic corpus created from monolingual data using back-translation. I evaluate the approach by fine-tuning a pre-trained language model on different proportions of real and synthetic data and measuring its performance on a held-out test set. My experiments show that fine-tuning a model on one-fifth real parallel data and synthetic parallel data effectively reduces occurrences of over-translation and bolsters the model's ability to translate in-domain terminology. My findings suggest that synthetic data can be a valuable resource for low-resource domain adaptation, especially when real parallel data is difficult to obtain. The proposed method can be extended to other low-resource domains where annotated data is scarce, potentially leading to more accurate models and better translation of these domains.

The recent advance in generative modeling in particular the unsupervised learning of data distribution is attributed to the invention of models with new learning algorithms. Among the methods proposed, generative adversarial networks (GANs) have shown to be the most efficient approaches to estimate data distributions. The core idea of GANs is an adversarial training of two deep neural networks, called generator and discriminator, to learn an implicit approximation of the true data distribution. The distribution is approximated through the weights of the generator network, and interaction with the distribution is through the process of sampling. GANs have found to be useful in applications such as image-to-image translation, in-painting, and text-to-image synthesis. In this thesis, we propose to capitalize on the power of GANs for different remote sensing problems. The first problem is a new research track to the remote sensing community that aims to generate remote sensing images from text descriptions. More specifically, we focus on exploiting ancient text descriptions of geographical areas, inherited from previous civilizations, and convert them the equivalent remote sensing images. The proposed method is composed of a text encoder and an image synthesis module. The text encoder is tasked with converting a text description into a vector. To this end, we explore two encoding schemes: a multilabel encoder and a doc2vec encoder. The multilabel encoder takes into account the presence or absence of objects in the encoding process whereas the doc2vec method encodes additional information available in the text. The encoded vectors are then used as conditional information to a GAN network and guide the synthesis process. We collected satellite images and ancient text descriptions for training in order to evaluate the efficacy of the proposed method. The qualitative and quantitative results obtained suggest that the doc2vec encoder-based model yields better images in terms of the semantic agreement with the input description. In addition, we present open research areas that we believe are important to further advance this new research area. The second problem we want to address is the issue of semi-supervised domain adaptation. The goal of domain adaptation is to learn a generic classifier for multiple related problems, thereby reducing the cost of labeling. To that end, we propose two methods. The first method uses GANs in the context of image-to-image translation to adapt source domain images into target domain images and train a classifier using the adapted images. We evaluated the proposed method on two remote sensing datasets. Though we have not explored this avenue extensively due to computational challenges, the results obtained show that the proposed method is promising and worth exploring in the future. The second domain adaptation strategy borrows the adversarial property of GANs to learn a new representation space where the domain discrepancy is negligible, and the new features are discriminative enough. The method is composed of a feature extractor, class predictor, and domain classifier blocks. Contrary to the traditional methods that perform representation and classifier learning in separate stages, this method combines both into a single-stage thereby learning a new representation of the input data that is domain invariant and discriminative. After training, the classifier is used to predict both source and target domain labels. We apply this method for large-scale land cover classification and cross-sensor hyperspectral classification problems. Experimental results obtained show that the proposed method provides a performance gain of up to 40%, and thus indicates the efficacy of the method.

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24027号 / 情博第783号 / 新制||情||133(附属図書館) / 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 西野 恒 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

With the rapid growth of computing power, approaches based on deep learning algorithms have achieved remarkable results in solving computer vision classification problems. These performance improvements are achieved by assuming the source and target data are collected from the same probability distribution. However, this assumption is usually too strict to be satisfied in many real-world applications, such as big data analysis, natural language processing, and computer vision classification problems. Because of distribution discrepancies between these domains, directly training the model on the source domain cannot be expected to generate satisfactory results on the target domain. Therefore, the problem of minimizing these data distribution discrepancies is the main challenge with which modern machine learning is now faced. To address this problem, domain adaptation (DA) aims to identify domain-invariant features between two different but related domains. This thesis proposes a state-of-the-art DA approach that overcomes the limitations of traditional DA methods. To capture fine-grained information for each category, I deploy centroid-to-centroid alignment to perform domain adaptation. An Exponential Moving Average strategy (EMA) is used to ensure we can form robust source and target centroids. A Gaussian-uniform mixture model is trained using an Expectation-Maximization (EM) algorithm to infer the robustness of the target pseudo-labels. With the help of target pseudo-labels, I propose two novel types of classifiers: (1) a target-oriented classifier (TO); and (2) a centroid-oriented classifier (CO). Extensive experiments show that these two classifiers exhibit superior performance on a variety of DA benchmarks when compared to standard baseline methods. / Master of Science / Approaches based on deep learning algorithms have achieved remarkable results in solving computer vision classification problems. These performance improvements are achieved by assuming the source and target data are collected from the same probability distribution; however, in many real-world applications, such as big data analysis, natural language processing, and computer vision classification problems, this assumption is usually too strict to be satisfied. For example, these two domains may have the same types of classes, but the objects in each category of these different domains can vary in shape, color, background, or even illumination. Because the probability distributions are slightly mismatched, directly training the model on one domain cannot achieve a satisfactory result on the other domain. To address this problem, domain adaptation (DA) aims to extract common features on both domains to transfer knowledge from one domain to another. In this thesis, I propose a state-of-the-art DA approach that overcomes the limitation of the traditional DA methods. To capture the low-level information of each category, I deploy centroid-to-centroid alignment to perform domain adaptation. An Exponential Moving Average (EMA) strategy is used to ensure the generation of robust centroids. A Gaussian-Uniform Mixture model is trained by using the Expectation-Maximization (EM) algorithm to infer the robustness of the target sample pseudo-labels. With the help of robust target pseudo-labels, I propose two novel types of classifiers: (1) a target-oriented classifier (TO); and (2) a centroid-oriented classifier (CO). Extensive experiments show that the proposed method outperforms traditional baseline methods on various DA benchmarks.

Deep Learning models have seen great application in demanding tasks such as machine translation and autonomous driving. However, building such models has proved challenging, both from a computational perspective and due to the requirement of a plethora of annotated data. Moreover, when challenged on new situations or data distributions (target domain), those models may perform inadequately. Such examples are transitioning from one city to another, different weather situations, or changes in sunlight. Unsupervised Domain adaptation (UDA) exploits unlabelled data (easy access) to adapt models to new conditions or data distributions. Inspired by the fact that environmental changes happen gradually, we focus on Online UDA. Instead of directly adjusting a model to a demanding condition, we constantly perform minor adaptions to every slight change in the data, creating a soft transition from the current domain to the target one. To perform gradual adaptation, we utilized state-of-the-art semantic segmentation approaches on increasing rain intensities (25, 50, 75, 100, and 200mm of rain). We demonstrate that deep learning models can adapt substantially better to hard domains when exploiting intermediate ones. Moreover, we introduce a model switching mechanism that allows adjusting back to the source domain, after adaptation, without dropping performance. / Deep Learning-modeller har sett stor tillämpning i krävande uppgifter som maskinöversättning och autonom körning. Att bygga sådana modeller har dock visat sig vara utmanande, både ur ett beräkningsperspektiv och på grund av kravet på en uppsjö av kommenterade data. Dessutom, när de utmanas i nya situationer eller datadistributioner (måldomän), kan dessa modeller prestera otillräckligt. Sådana exempel är övergång från en stad till en annan, olika vädersituationer eller förändringar i solljus. Unsupervised Domain adaptation (UDA) utnyttjar omärkt data (enkel åtkomst) för att anpassa modeller till nya förhållanden eller datadistributioner. Inspirerade av att miljöförändringar sker gradvis, fokuserar vi på Online UDA. Istället för att direkt anpassa en modell till ett krävande tillstånd, gör vi ständigt mindre anpassningar till varje liten förändring i data, vilket skapar en mjuk övergång från den aktuella domänen till måldomänen. För att utföra gradvis anpassning använde vi toppmoderna semantiska segmenteringsmetoder för att öka regnintensiteten (25, 50, 75, 100 och 200 mm regn). Vi visar att modeller för djupinlärning kan anpassa sig betydligt bättre till hårda domäner när man utnyttjar mellanliggande. Dessutom introducerar vi en modellväxlingsmekanism som tillåter justering tillbaka till källdomänen, efter anpassning, utan att tappa prestanda.

Autonomous driving is a rapidly changing industry and has recently become a heavily focused research topic for vehicle producing companies and research organizations. These autonomous vehicles are typically equipped with sensors such as Light Detection and Radar (LiDAR) in order to perceive their surroundings. The problem of detecting and classifying surrounding objects from the sensor data can be solved using different types of algorithms. Recently, machine learning solutions have been investigated. One problem with the machine learning approach is that the models usually require a substantial amount of labeled data, and labeling LiDAR data is a time-consuming process. A promising solution to this problem is utilizing Domain Adaptation (DA) methods. The DA methods can use labeled camera data, which are easier to label, in conjunction with unlabeled LiDAR data to improve the performance of machine learning models on LiDAR data. This thesis investigates and compares different DA methods that can be used for classification of LiDAR data. In this thesis, two image classification datasets with data of humans and vehicles were created. One dataset contains camera images, and the other dataset contains LiDAR intensity images. The datasets were used to train and test three methods: (1) a baseline method, which simply uses labeled camera images to train a model. (2) Correlation Alignment (CORAL), a DA method that aligns the covariance of camera features towards LiDAR features. (3) Deep Adaptation Network (DAN), a DA method that includes a maximum mean discrepancy computation between camera and LiDAR features within the objective function of the model. These methods were then evaluated based on the resulting confusion matrices, accuracy, recall, precision and F1-score on LiDAR data. The results showed that DAN was the best out of the three methods, reaching an accuracy of 87% while the baseline and CORAL only measured at 65% and 73%, respectively. The strong performance of DAN showed that there is potential for using DA methods within the field of autonomous vehicles. / Industrin för självkörande fordon är snabbt förändlig och har under de senaste åren fått ett enormt fokus från biltillverkare och forskningsorganisationer. De självkörande fordonen är oftast utrustade med sensorer som Light Detection and Radar (LiDAR) för att hjälpa fordonen förstå omgivningen. Klassificering och identifiering av omgivande objekt är ett problem som kan lösas med hjälp av olika slags algoritmer. Nyligen har lösningar som utnyttjar maskininlärning undersökts. Ett problem med dessa lösningar är att modellerna oftast kräver en enorm mängd annoterad data, och att annotera LiDAR-data är en kostsam process. En lösning till detta problem är att utnyttja metoder inom Domain Adaptation (DA). DA metoder kan utnyttja både annoterad kameradata samt oannoterad LiDAR-data för att förbättra modellernas prestanda på LiDAR-data. Den här avhandlingen undersöker och jämför olika metoder inom DA som kan användas för att klassificera LiDAR-data. I det här arbetet skapades två dataset som består av data från människor och fordon. Det ena datasettet innehöll kamerabilder och det andra innehöll LiDAR-intensitetsbilder. Dessa dataset användes för att träna och testa tre olika metoder: (1) en baselinemetod, som endast använde annoterade kamerabilder för att träna en modell. (2) Correlation Alignment (CORAL), en metod inom DA som justerar kovariansen hos kamerafeatures mot kovariansen hos LiDAR-features. (3) Deep Adaptation Network (DAN), en metod inom DA som lägger till en uträkning av maximum mean discrepancy mellan kamerafeatures och LiDAR-features i modellens optimeringskriterie. Metoderna bedömdes sedan beroende på deras förvirringsmatriser, träffsäkerhet, precision, täckning och F1-träffsäkerhet på LiDAR-data. Resultaten avslöjade att DAN presterade bäst av de tre metoderna och uppnåde 87% träffsäkerhet medan baselinemetoden och CORAL bara uppnådde 65% respektive 73%. DANs imponerande prestation visade att det finns potential för att använda metoder inom DA för självkörande fordon.