Domain adaptation on semantic segmentation generally refers to the procedures for narrowing the distribution gap between source and target data, which is vital for developing the automatic vehicle system. It requires a large amount of data with well-labelled ground truth at the pixel level. Labelling this scale of data is extremely costly due to the lot of human effort required. Also, manually labelling often comes with label noises that are harmful to automatic vehicle system development. In this case, solving the above problem utilizes computer-generated data and ground truth for development. However, a notorious problem exists when a system is trained with synthetic data but deployed in a real-world environment, which results from the distribution (domain) difference between these two kinds of data, and domain adaptation helps solve this issue. In the thesis, the limitation of conventional batch normalization layer on adversarial learning based domain adaptation methods is mentioned and discussed. From the view of the limitation, we propose replacing the Sharing Affine Transformation with our proposed Separate Affine Transformation (SEAT) to improve the domain adapting performance. The proposed SEAT is simple, easily implemented, and integrated into existing adversarial learning-based unsupervised domain adaptation methods. Also, to further improve the adaptation quality on lower-level features, we introduce multi-level adaptation by adding the lower-level features to the higher-level ones before feeding them to the discriminator, which is different from others by adding extra discriminators. Finally, a simple training strategy, self-training, is adopted to improve the model performance further. Extensive experiments show that our proposed method is able to get comparable results with other domain adaptation methods with simpler design.

Master of Science / Department of Computing and Information Sciences / Doina Caragea / Machine learning is the subfield of Artificial intelligence that gives computers the ability to learn without being explicitly programmed, as it was defined by Arthur Samuel - the American pioneer in the field of computer gaming and artificial intelligence who was born in Emporia, Kansas. Supervised Machine Learning is focused on building predictive models given labeled training data. Data may come from a variety of sources, for instance, social media networks. In our research, we use Twitter data, specifically, user-generated tweets about disasters such as floods, hurricanes, terrorist attacks, etc., to build classifiers that could help disaster management teams identify useful information. A supervised classifier trained on data (training data) from a particular domain (i.e. disaster) is expected to give accurate predictions on unseen data (testing data) from the same domain, assuming that the training and test data have similar characteristics. Labeled data is not easily available for a current target disaster. However, labeled data from a prior source disaster is presumably available, and can be used to learn a supervised classifier for the target disaster. Unfortunately, the source disaster data and the target disaster data may not share the same characteristics, and the classifier learned from the source may not perform well on the target. Domain adaptation techniques, which use unlabeled target data in addition to labeled source data, can be used to address this problem. We study single-source and multi-source domain adaptation techniques, using Nave Bayes classifier. Experimental results on Twitter datasets corresponding to six disasters show that domain adaptation techniques improve the overall performance as compared to basic supervised learning classifiers. Domain adaptation is crucial for many machine learning applications, as it enables the use of unlabeled data in domains where labeled data is not available.

A main problem in machine learning is to predict the response variables of a test set given the training data and its corresponding response variables. A predictive model can perform satisfactorily only if the training data is an appropriate representative of the test data. This intuition is re???ected in the assumption that the training data and the test data are drawn from the same underlying distribution. However, the assumption may not be correct in many applications for various reasons. For example, gathering training data from the test population might not be easily possible, due to its expense or rareness. Or, factors like time, place, weather, etc can cause the difference in the distributions. I propose a method based on kernel distribution embedding and Hilbert Schmidt Independence Criteria (HSIC) to address this problem. The proposed method explores a new representation of the data in a new feature space with two properties: (i) the distributions of the training and the test data sets are as close as possible in the new feature space, (ii) the important structural information of the data is preserved. The algorithm can reduce the dimensionality of the data while it preserves the aforementioned properties and therefore it can be seen as a dimensionality reduction method as well. Our method has a closed-form solution and the experimental results on various data sets show that it works well in practice.

Recognizing visual information is crucial for many real artificial-intelligence-based applications, ranging from domestic robots to autonomous vehicles. However, the success of deep learning methods on visual recognition tasks is highly dependent on access to large-scale labeled datasets, which are expensive and cumbersome to collect. Transfer learning provides a way to alleviate the burden of annotating data, which transfers the knowledge learned from a rich-labeled source domain to a scarce-labeled target domain. However, the performance of deep learning models degrades significantly when testing on novel domains due to the presence of domain shift. To tackle the domain shift, conventional domain adaptation methods diminish the domain shift between two domains with a distribution matching loss or adversarial loss. These models align the domain-specific feature distribution and the domain-invariant feature distribution simultaneously, which is sub-optimal towards solving deep domain adaptation tasks, given that deep neural networks are known to extract features in which multiple hidden factors are highly entangled. This thesis explores how to learn effective transferable features by disentangling the deep features. The following questions are studied: (1) how to disentangle the deep features into domain-invariant and domain-specific features? (2) how would feature disentanglement help to learn transferable features under a synthetic-to-real domain adaptation scenario? (3) how would feature disentanglement facilitate transfer learning with multiple source or target domains? (4) how to leverage feature disentanglement to boost the performance in a federated system? To address these needs, this thesis proposes deep adversarial feature disentanglement: a class/domain identifier is trained on the labeled source domain and the disentangler generates features to fool the class/domain identifier. Extensive experiments and empirical analysis demonstrate the effectiveness of the feature disentanglement method on many real-world domain adaptation tasks. Specifically, the following three unsupervised domain adaptation scenarios are explored: (1) domain agnostic learning with disentangled representations, (2) unsupervised federated domain adaptation, (3) multi-source domain adaptation.

Indiana University-Purdue University Indianapolis (IUPUI) / Domain adaptation is one of the hottest directions in solving annotation insufficiency problem of deep learning. General domain adaptation is not consistent with the practical scenarios in the industry. In this thesis, we focus on two concerns as below. First is that labeled data are generally collected from multiple domains. In other words, multi-source adaptation is a more common situation. Simply extending these single-source approaches to the multi-source cases could cause sub-optimal inference, so specialized multi-source adaptation methods are essential. The main challenge in the multi-source scenario is a more complex divergence situation. Not only the divergence between target and each source plays a role, but the divergences among distinct sources matter as well. However, the significance of maintaining consistency among multiple sources didn't gain enough attention in previous work. In this thesis, we propose an Enhanced Consistency Multi-Source Adaptation (EC-MSA) framework to address it from three perspectives. First, we mitigate feature-level discrepancy by cross-domain conditional alignment, narrowing the divergence between each source and target domain class-wisely. Second, we enhance multi-source consistency via dual mix-up, diminishing the disagreements among different sources. Third, we deploy a target distilling mechanism to handle the uncertainty of target prediction, aiming to provide high-quality pseudo-labeled target samples to benefit the previous two aspects. Extensive experiments are conducted on several common benchmark datasets and demonstrate that our model outperforms the state-of-the-art methods. Second is that data privacy and security is necessary in practice. That is, we hope to keep the raw data stored locally while can still obtain a satisfied model. In such a case, the risk of data leakage greatly decreases. Therefore, it is natural for us to combine the federated learning paradigm with domain adaptation. Under the source-private setting, the main challenge for us is to expose information from the source domain to the target domain while make sure that the communication process is safe enough. In this thesis, we propose a method named Fourier Transform-Assisted Federated Domain Adaptation (FTA-FDA) to alleviate the difficulties in two ways. We apply Fast Fourier Transform to the raw data and transfer only the amplitude spectra during the communication. Then frequency space interpolations between these two domains are conducted, minimizing the discrepancies while ensuring the contact of them and keeping raw data safe. What's more, we make prototype alignments by using the model weights together with target features, trying to reduce the discrepancy in the class level. Experiments on Office-31 demonstrate the effectiveness and competitiveness of our approach, and further analyses prove that our algorithm can help protect privacy and security.

abstract: Endowing machines with the ability to understand digital images is a critical task for a host of high-impact applications, including pathology detection in radiographic imaging, autonomous vehicles, and assistive technology for the visually impaired. Computer vision systems rely on large corpora of annotated data in order to train task-specific visual recognition models. Despite significant advances made over the past decade, the fact remains collecting and annotating the data needed to successfully train a model is a prohibitively expensive endeavor. Moreover, these models are prone to rapid performance degradation when applied to data sampled from a different domain. Recent works in the development of deep adaptation networks seek to overcome these challenges by facilitating transfer learning between source and target domains. In parallel, the unification of dominant semi-supervised learning techniques has illustrated unprecedented potential for utilizing unlabeled data to train classification models in defiance of discouragingly meager sets of annotated data. In this thesis, a novel domain adaptation algorithm -- Domain Adaptive Fusion (DAF) -- is proposed, which encourages a domain-invariant linear relationship between the pixel-space of different domains and the prediction-space while being trained under a domain adversarial signal. The thoughtful combination of key components in unsupervised domain adaptation and semi-supervised learning enable DAF to effectively bridge the gap between source and target domains. Experiments performed on computer vision benchmark datasets for domain adaptation endorse the efficacy of this hybrid approach, outperforming all of the baseline architectures on most of the transfer tasks. / Dissertation/Thesis / Masters Thesis Computer Science 2019

<p>With timeliness and efficiency being critical in the aviation maintenance industry, the need has been growing for smart technological solutions that help in optimizing and streamlining the different underlying tasks. One such task is the technical documentation of the performed maintenance operations. Instead of paper-based documentation, voice tools that transcribe spoken logbook entries allow technicians to document their work right away in a hands-free and time efficient manner. However, an accurate automatic speech recognition (ASR) model requires large training corpora, which are lacking in the domain of aviation maintenance. In addition, ASR models which are trained on huge corpora in standard English perform poorly in such a technical domain with non-standard terminology. Hence, this thesis investigates the extent to which fine-tuning an ASR model, pre-trained on standard English corpora, on limited in-domain data improves its recognition performance in the technical domain of aviation maintenance. The thesis presents a case study on one such pre-trained ASR model, wav2vec 2.0. Results show that fine-tuning the model on a limited anonymized dataset of maintenance logbook entries brings about a significant reduction in its error rates when tested on not only an anonymized in-domain dataset, but also a non-anonymized one. This suggests that any available aviation maintenance logbooks, even if anonymized for privacy, can be used to fine-tune general-purpose ASR models and enhance their in-domain performance. Lastly, an analysis on the influence of voice characteristics on model performance stresses the need for balanced datasets representative of the population of aviation maintenance technicians.</p>

Esta tesis contribuye en el límite del conocimiento en algunos procesos relevantes dentro del flujo de trabajo típico asociado a los sistemas de reconocimiento óptico de música (OMR). El análisis de los documentos es una etapa clave y temprana dentro de dicho flujo, cuyo objetivo es proporcionar una versión simplificada de la información entrante; es decir, de las imágenes de documentos musicales. El resto de procesos involucrados en OMR pueden aprovechar esta simplificación para resolver sus correspondientes tareas de forma más sencilla y centrándose únicamente en la información que necesitan. Un ejemplo claro es el proceso dedicado a reconocer las áreas donde se sitúan los diferentes pentagramas. Tras obtener las coordenadas de los mismos, los pentagramas individuales pueden ser procesados para recuperar la secuencia simbólica musical que contienen y así construir una versión digital de su contenido. El trabajo de investigación que se ha realizado para completar la presente tesis se encuentra avalada por una serie de contribuciones publicadas en revistas de alto impacto y congresos internacionales. Concretamente, esta tesis contiene un conjunto de 4 artículos que se han publicado en revistas indexadas en el Journal Citation Reports y situadas en los primeros cuartiles en cuanto al factor de impacto, teniendo un total de 58 citas según Google Scholar. También se han incluido 3 comunicaciones realizadas en diferentes ediciones de un congreso internacional de Clase A según la clasificación proporcionada por GII-GRIN-SCIE. Se puede observar que las publicaciones tratan temas muy relacionados entre sí, enfocándose principalmente en el análisis de documentos orientado a OMR pero con pinceladas de transcripción de la secuencia musical y técnicas de adaptación al dominio. También hay publicaciones que demuestran que algunas de estas técnicas pueden ser aplicadas a otros tipos de imágenes de documentos, haciendo que las soluciones propuestas sean más interesantes por su capacidad de generalización y adaptación a otros contextos. Además del análisis de documentos, también se estudia cómo afectan estos procesos a la transcripción final de la notación musical, que a fin de cuentas, es el objetivo final de los sistemas OMR, pero que hasta el momento no se había investigado. Por último, debido a la incontable cantidad de información que requieren las redes neuronales para construir un modelo suficientemente robusto, también se estudia el uso de técnicas de adaptación al dominio, con la esperanza de que su éxito abra las puertas a la futura aplicabilidad de los sistemas OMR en entornos reales. Esto es especialmente interesante en el contexto de OMR debido a la gran cantidad de documentos sin datos de referencia que son necesarios para entrenar modelos de redes neuronales, por lo que una solución que aproveche las limitadas colecciones etiquetadas para procesar documentos de otra índole nos permitiría un uso más práctico de estas herramientas de transcripción automáticas. Tras la realización de esta tesis, se observa que la investigación en OMR no ha llegado al límite que la tecnología puede alcanzar y todavía hay varias vías por las que continuar explorando. De hecho, gracias al trabajo realizado, se han abierto incluso nuevos horizontes que se podrían estudiar para que algún día estos sistemas puedan ser utilizados para digitalizar y transcribir de forma automática la herencia musical escrita o impresa a gran escala y en un tiempo razonable. Entre estas nuevas líneas de investigación, podemos destacar las siguientes: · En esta tesis se han publicado contribuciones que utilizan una técnica de adaptación al dominio para realizar análisis de documentos con buenos resultados. La exploración de nuevas técnicas de adaptación al dominio podría ser clave para construir modelos de redes neuronales robustos y sin la necesidad de etiquetar manualmente una parte de todas las obras musicales que se pretenden digitalizar. · La aplicación de las técnicas de adaptación al dominio en otros procesos como en la transcripción de la secuencia musical podría facilitar el entrenamiento de modelos capaces de realizar esta tarea. Los algoritmos de aprendizaje supervisado requieren que personal cualificado se encargue de transcribir manualmente una parte de las colecciones, pero los costes temporal y económico asociados a este proceso suponen un amplio esfuerzo si el objetivo final es transcribir todo este patrimonio cultural. Por ello, sería interesante estudiar la aplicabilidad de estas técnicas con el fin de reducir drásticamente esta necesidad. · Durante la tesis, se ha estudiado cómo afecta el factor de escala de los documentos en el rendimiento de varios procesos de OMR. Además de la escala, otro factor importante que se debe tratar es la orientación, ya que las imágenes de los documentos no siempre estarán perfectamente alineadas y pueden sufrir algún tipo de rotación o deformación que provoque errores en la detección de la información. Por lo tanto, sería interesante estudiar cómo afectan estas deformaciones a la transcripción y encontrar soluciones viables para el contexto que aplica. · Como caso general y más básico, se ha estudiado cómo, con diferentes modelos de propósito general de detección de objetos, se podrían extraer los pentagramas para su posterior procesamiento. Estos elementos se han considerado rectangulares y sin rotación, pero hay que tener en cuenta que no siempre nos encontraremos con esta situación. Por lo tanto, otra posible vía de investigación sería estudiar otros tipos de modelos que permitan detectar elementos poligonales y no solo rectangulares, así como la posibilidad de detectar objetos con cierta inclinación sin introducir solapamiento entre elementos consecutivos como ocurre en algunas herramientas de etiquetado manual como la utilizada en esta tesis para la obtención de datos etiquetados para experimentación: MuRET. Estas líneas de investigación son, a priori, factibles pero es necesario realizar un proceso de exploración con el fin de detectar aquellas técnicas útiles para ser adaptadas al ámbito de OMR. Los resultados obtenidos durante la tesis señalan que es posible que estas líneas puedan aportar nuevas contribuciones en este campo, y por ende, avanzar un paso más a la aplicación práctica y real de estos sistemas a gran escala.

Learning from unlabeled data is a topic of critical significance in machine learning, as the large datasets required to train ever-growing models are costly and impractical to annotate. Semi-Supervised Learning (SSL) methods aim to learn from a few labels and a large unlabeled dataset. In another approach, Domain Adaptation (DA) leverages data from a similar source domain to train a model for a target domain. This thesis focuses on Semi-Supervised Domain Adaptation (SSDA) for the dense task of semantic segmentation, where labels are particularly costly to obtain. SSDA has not received much attention yet, even though it has a great potential and represents a realistic scenario. The few existing SSDA methods for semantic segmentation reuse ideas from Unsupervised DA, despite the di↵erences between the two settings. This thesis proposes a new semantic segmentation framework designed particularly for the SSDA setting. The approach followed was to forego domain alignment and focus instead on enhancing clusterability of target domain features, an idea from SSL. The method is based on consistency regularization, combined with pixel contrastive learning and self-training. The proposed framework is found to be e↵ective not only in SSDA, but also in SSL. Ultimately, a unified solution for SSL and SSDA semantic segmentation is presented. Experiments were conducted on the target dataset of Cityscapes and source dataset of GTA5. The method proposed is competitive in both SSL and SSDA, and sets a new state-of-the-art for SSDA achieving a 65.6% mIoU (+4.4) on Cityscapes with 100 labeled samples. This thesis has an immediate impact on practical applications by proposing a new best-performing framework for the under-explored setting of SSDA. Furthermore, it also contributes towards the more ambitious goal of designing a unified solution for learning from unlabeled data. / Inlärning med hjälp av omärkt data är ett område av stor vikt inom maskininlärning. Detta på grund av att de stora datamängder som blivit nödvändiga för att träna konstant växande modeller både är kostsamma och opraktiska att implementera. Målet med Semi-Supervised Learning (SSL) är att kombinera ett fåtal etiketter med en stor mängd omärkt data för inlärning. Som ett annat tillvägagångssätt använder Domain Adaptation (DA) data från en liknande domän för att träna en annan måldomän. I Denna avhandling används Semi-Supervised Domain Adaptation (SSDA) för att utföra sådan semantisk segmentering, i vilken etiketter är särskilt kostsamma att erhålla. SSDA är ännu inte genererat mycket uppmärksamhet, även om det har en stor potential och representerar ett realistiskt scenario. De få metoder av SSDA som existerar för semantisk segmentering återanvänder idéer från Unsupervised DA, trots de olikheter som finns mellan de två modellerna. Denna avhandling föreslår ett nytt ramverk för semantisk segmentering, designat speciellt för SSDA modellen. Detta genom att försaka domänanpassning och i stället fokusera på att förbättra klusterbarheten av måldomänens egenskaper, en idé tagen från SSL. Metoden är baserad på konsistensregularisering, i kombination med pixelkontrastinlärning och självinlärning. Det föreslagna ramverket visar sig vara effektivt, inte bara för SSDA, men även för SSL. Till slut presenteras en enad lösning för semantisk segmentering med SLL och SSDA. Experiment utfördes på måldata från Cityscapes samt källdata från GTA5. Den föreslagna metoden är konkurrenskraftig både för SSL och SSDA, och blir världsledande för SSDA genom att uppnå 65,6% mIoU (+4,4) för Cityscapes med 100 märkta testdata. Denna avhandling har en omedelbar effekt gällande praktiska applikationer genom att föreslå ett nytt ”bäst resulterande” ramverk för dåligt utforskade inställningar av SSDA. Till yttermera visso bidrar avhandlingen även till det mer ambitiösa målet att designa en enad lösning för maskininlärning från omärkta data.

In the field of machine learning (ML) a very commonly encountered problem is the lack of generalizability of learnt classification functions when subjected to new samples that are not representative of the training distribution. The discrepancy between the training (a.k.a. source) and test (a.k.a.target) distributions are caused by several latent factors such as change in appearance, illumination, viewpoints and so on, which is also popularly known as domain-shift. In order to make a classifier cope with such domain-shifts, a sub-field in machine learning called domain adaptation (DA) has emerged that jointly uses the annotated data from the source domain together with the unlabelled data from the target domain of interest. For a classifier to be adapted to an unlabelled target data set is of tremendous practical significance because it has no associated labelling cost and allows for more accurate predictions in the environment of interest. A majority of the DA methods which address the single source and single target domain scenario are not easily extendable to many practical DA scenarios. As there has been as increasing focus to make ML models deployable, it calls for devising improved methods that can handle inherently complex practical DA scenarios in the real world. In this work we build towards this goal of addressing more practical DA settings and help realize novel methods for more real world applications: (i) We begin our work with analyzing and addressing the single source and single target setting by proposing whitening-based embedded normalization layers to align the marginal feature distributions between two domains. To better utilize the unlabelled target data we propose an unsupervised regularization loss that encourages both confident and consistent predictions. (ii) Next, we build on top of the proposed normalization layers and use them in a generative framework to address multi-source DA by posing it as an image translation problem. This proposed framework TriGAN allows a single generator to be learned by using all the source domain data into a single network, leading to better generation of target-like source data. (iii) We address multi-target DA by learning a single classifier for all of the target domains. Our proposed framework exploits feature aggregation with a graph convolutional network to align feature representations of similar samples across domains. Moreover, to counteract the noisy pseudo-labels we propose to use a co-teaching strategy with a dual classifier head. To enable smoother adaptation, we propose a domain curriculum learning ,when the domain labels are available, that adapts to one target domain at a time, with increasing domain gap. (iv) Finally, we address the challenging source-free DA where the only source of supervision is a source-trained model. We propose to use Laplace Approximation to build a probabilistic source model that can quantify the uncertainty in the source model predictions on the target data. The uncertainty is then used as importance weights during the target adaptation process, down-weighting target data that do not lie in the source manifold.