Дисертації з теми "Generative competitive neural network"
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Гайдук, Ірина Вадимівна. "Вирішення транспортної задачі методами машинного навчання". Master's thesis, КПІ ім. Ігоря Сікорського, 2021. https://ela.kpi.ua/handle/123456789/46504.
Повний текст джерелаMaster’s thesis: 87 pages, 27 figures, 24 tables, 21 sources. Theme: The classical problem of optimal transportation. The conducted research solves it by known methods, their advantages and disadvantages, the necessary conditions for the existence of an optimal solution. This was a proposed machine method for solving problems with the construction and model of learning based on a generative neural network. The paper considered general information on the method of solving the problem of optimal transportation with its unbalance and scalability. The results of three different types of problems solved by the machine learning method were analyzed. The subject of the study is the classical problem of optimal transportation in three different types. The subject of research is the methods of machine learning, in particular the generative competitive neural network.
Liu, Mengxin. "Generative Neural Network for Portfolio Optimization." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-53027.
Повний текст джерелаYamazaki, Hiroyuki Vincent. "On Depth and Complexity of Generative Adversarial Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217293.
Повний текст джерелаTrots att Generative Adversarial Networks (GAN) har lyckats generera realistiska bilder består de än idag av neurala nätverk som är parametriserade med relativt få tränbara vikter jämfört med neurala nätverk som används för klassificering. Vi tror att en sådan modell är suboptimal vad gäller generering av högdimensionell och komplicerad data och anser att modeller med högre kapaciteter bör ge bättre estimeringar. Dessutom, i en generativ uppgift så förväntas en modell kunna extrapolera information från lägre till högre dimensioner medan i en klassificeringsuppgift så behöver modellen endast att extrahera lågdimensionell information från högdimensionell data. Vi evaluerar ett flertal GAN med varierande kapaciteter genom att använda shortcut connections för att studera hur kapaciteten påverkar träningsstabiliteten, samt kvaliteten av de genererade datapunkterna. Resultaten visar att träningen blir mindre stabil för modeller som fått högre kapaciteter genom naivt tillsatta lager men visar samtidigt att datapunkternas kvaliteter kan öka, specifikt för bilder, bilder med hög visuell fidelitet. Detta åstadkoms med hjälp utav regularisering och noggrann balansering.
Aftab, Nadeem. "Disocclusion Inpainting using Generative Adversarial Networks." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-40502.
Повний текст джерелаAmartur, Sundar C. "Competitive recurrent neural network model for clustering of multispectral data." Case Western Reserve University School of Graduate Studies / OhioLINK, 1995. http://rave.ohiolink.edu/etdc/view?acc_num=case1058445974.
Повний текст джерелаDaley, Jr John. "Generating Synthetic Schematics with Generative Adversarial Networks." Thesis, Högskolan Kristianstad, Fakulteten för naturvetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hkr:diva-20901.
Повний текст джерелаIonascu, Beatrice. "Modelling user interaction at scale with deep generative methods." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239333.
Повний текст джерелаFörståelse för hur användare interagerar med ett företags tjänst är essentiell för data-drivna affärsverksamheter med ambitioner om att bättre tillgodose dess användare och att förbättra deras utbud. Generativ maskininlärning möjliggör modellering av användarbeteende och genererande av ny data i syfte att simulera eller identifiera och förklara typiska användarmönster. I detta arbete introducerar vi ett tillvägagångssätt för storskalig modellering av användarinteraktion i en klientservice-modell. Vi föreslår en ny representation av multivariat tidsseriedata i form av tidsbilder vilka representerar temporala korrelationer via spatial organisering. Denna representation delar två nyckelegenskaper som faltningsnätverk har utvecklats för att exploatera, vilket tillåter oss att utveckla ett tillvägagångssätt baserat på på djupa generativa modeller som bygger på faltningsnätverk. Genom att introducera detta tillvägagångssätt för tidsseriedata expanderar vi applicering av faltningsnätverk inom domänen för multivariat tidsserie, specifikt för användarinteraktionsdata. Vi använder ett tillvägagångssätt inspirerat av ramverket β-VAE i syfte att lära modellen gömda faktorer som definierar olika användarmönster. Vi utforskar olika värden för regulariseringsparametern β och visar att det är möjligt att konstruera en modell som lär sig en latent representation av identifierbara och multipla användarbeteenden. Vi visar med verklig data att modellen genererar realistiska exempel vilka i sin tur fångar statistiken på populationsnivå hos användarinteraktionsdatan, samt lär olika användarbeteenden och bidrar med precisa imputationer av saknad data.
Pagliarini, Silvia. "Modeling the neural network responsible for song learning." Thesis, Bordeaux, 2021. http://www.theses.fr/2021BORD0107.
Повний текст джерелаDuring the first period of their life, babies and juvenile birds show comparable phases of vocal development: first, they listen to their parents/tutors in order to build a neural representation of the experienced auditory stimulus, then they start to produce sound and progressively get closer to reproducing their tutor song. This phase of learning is called the sensorimotor phase and is characterized by the presence of babbling, in babies, and subsong, in birds. It ends when the song crystallizes and becomes similar to the one produced by the adults.It is possible to find analogies between brain pathways responsible for sensorimotor learning in humans and birds: a vocal production pathway involves direct projections from auditory areas to motor neurons, and a vocal learning pathway is responsible for imitation and plasticity. The behavioral studies and the neuroanatomical structure of the vocal control circuit in humans and birds provide the basis for bio-inspired models of vocal learning.In particular, birds have brain circuits exclusively dedicated to song learning, making them an ideal model for exploring the representation of vocal learning by imitation of tutors.This thesis aims to build a vocal learning model underlying song learning in birds. An extensive review of the existing literature is discussed in the thesis: many previous studies have attempted to implement imitative learning in computational models and share a common structure. These learning architectures include the learning mechanisms and, eventually, exploration and evaluation strategies. A motor control function enables sound production and sensory response models either how sound is perceived or how it shapes the reward. The inputs and outputs of these functions lie (1)~in the motor space (motor parameters’ space), (2)~in the sensory space (real sounds) and (3)~either in the perceptual space (a low dimensional representation of the sound) or in the internal representation of goals (a non-perceptual representation of the target sound).The first model proposed in this thesis is a theoretical inverse model based on a simplified vocal learning model where the sensory space coincides with the motor space (i.e., there is no sound production). Such a simplification allows us to investigate how to introduce biological assumptions (e.g. non-linearity response) into a vocal learning model and which parameters influence the computational power of the model the most. The influence of the sharpness of auditory selectivity and the motor dimension are discussed.To have a complete model (which is able to perceive and produce sound), we needed a motor control function capable of reproducing sounds similar to real data (e.g. recordings of adult canaries). We analyzed the capability of WaveGAN (a Generative Adversarial Network) to provide a generator model able to produce realistic canary songs. In this generator model, the input space becomes the latent space after training and allows the representation of a high-dimensional dataset in a lower-dimensional manifold. We obtained realistic canary sounds using only three dimensions for the latent space. Among other results, quantitative and qualitative analyses demonstrate the interpolation abilities of the model, which suggests that the generator model we studied can be used as a motor function in a vocal learning model.The second version of the sensorimotor model is a complete vocal learning model with a full action-perception loop (i.e., it includes motor space, sensory space, and perceptual space). The sound production is performed by the GAN generator previously obtained. A recurrent neural network classifying syllables serves as the perceptual sensory response. Similar to the first model, the mapping between the perceptual space and the motor space is learned via an inverse model. Preliminary results show the influence of the learning rate when different sensory response functions are implemented
Gustafsson, Alexander, and Jonatan Linberg. "Investigation of generative adversarial network training : The effect of hyperparameters on training time and stability." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-19847.
Повний текст джерелаZheng, Yilin. "Text-Based Speech Video Synthesis from a Single Face Image." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1572168353691788.
Повний текст джерелаNgo, Ho Anh Khoa. "Generative Probabilistic Alignment Models for Words and Subwords : a Systematic Exploration of the Limits and Potentials of Neural Parametrizations." Electronic Thesis or Diss., université Paris-Saclay, 2021. http://www.theses.fr/2021UPASG014.
Повний текст джерелаAlignment consists of establishing a mapping between units in a bitext, combining a text in a source language and its translation in a target language. Alignments can be computed at several levels: between documents, between sentences, between phrases, between words, or even between smaller units end when one of the languages is morphologically complex, which implies to align fragments of words (morphemes). Alignments can also be considered between more complex linguistic structures such as trees or graphs. This is a complex, under-specified task that humans accomplish with difficulty. Its automation is a notoriously difficult problem in natural language processing, historically associated with the first probabilistic word-based translation models. The design of new models for natural language processing, based on distributed representations computed by neural networks, allows us to question and revisit the computation of these alignments. This research project, therefore, aims to comprehensively understand the limitations of existing statistical alignment models and to design neural models that can be learned without supervision to overcome these drawbacks and to improve the state of art in terms of alignment accuracy
Nilsson, Mårten. "Augmenting High-Dimensional Data with Deep Generative Models." Thesis, KTH, Robotik, perception och lärande, RPL, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233969.
Повний текст джерелаDataaugmentering är en teknik som kan utföras på flera sätt för att förbättra träningen av diskriminativa modeller. De senaste framgångarna inom djupa generativa modeller har öppnat upp nya sätt att augmentera existerande dataset. I detta arbete har ett ramverk för augmentering av annoterade dataset med hjälp av djupa generativa modeller föreslagits. Utöver detta så har en metod för kvantitativ evaulering av kvaliteten hos genererade data set tagits fram. Med hjälp av detta ramverk har två dataset för pupillokalisering genererats med olika generativa modeller. Både väletablerade modeller och en ny modell utvecklad för detta syfte har testats. Den unika modellen visades både kvalitativt och kvantitativt att den genererade de bästa dataseten. Ett antal mindre experiment på standardiserade dataset visade exempel på fall där denna generativa modell kunde förbättra prestandan hos en existerande diskriminativ modell. Resultaten indikerar att generativa modeller kan användas för att augmentera eller ersätta existerande dataset vid träning av diskriminativa modeller.
Oskarsson, Joel. "Probabilistic Regression using Conditional Generative Adversarial Networks." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166637.
Повний текст джерелаLaifa, Oumeima. "A joint discriminative-generative approach for tumour angiogenesis assessment in computational pathology." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS230.
Повний текст джерелаAngiogenesis is the process through which new blood vessels are formed from pre-existing ones. During angiogenesis, tumour cells secrete growth factors that activate the proliferation and migration of endothelial cells and stimulate over production of the vascular endothelial growth factor (VEGF). The fundamental role of vascular supply in tumour growth and anti-cancer therapies makes the evaluation of angiogenesis crucial in assessing the effect of anti-angiogenic therapies as a promising anti-cancer therapy. In this study, we establish a quantitative and qualitative panel to evaluate tumour blood vessels structures on non-invasive fluorescence images and histopathological slide across the full tumour to identify architectural features and quantitative measurements that are often associated with prediction of therapeutic response. We develop a Markov Random Field (MFRs) and Watershed framework to segment blood vessel structures and tumour micro-enviroment components to assess quantitatively the effect of the anti-angiogenic drug Pazopanib on the tumour vasculature and the tumour micro-enviroment interaction. The anti-angiogenesis agent Pazopanib was showing a direct effect on tumour network vasculature via the endothelial cells crossing the whole tumour. Our results show a specific relationship between apoptotic neovascularization and nucleus density in murine tumor treated by Pazopanib. Then, qualitative evaluation of tumour blood vessels structures is performed in whole slide images, known to be very heterogeneous. We develop a discriminative-generative neural network model based on both learning driven model convolutional neural network (CNN), and rule-based knowledge model Marked Point Process (MPP) to segment blood vessels in very heterogeneous images using very few annotated data comparing to the state of the art. We detail the intuition and the design behind the discriminative-generative model, and we analyze its similarity with Generative Adversarial Network (GAN). Finally, we evaluate the performance of the proposed model on histopathology slide and synthetic data. The limits of this promising framework as its perspectives are shown
Käll, Viktor, and Erik Piscator. "Particle Filter Bridge Interpolation in GANs." Thesis, KTH, Matematisk statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301733.
Повний текст джерелаGenerative adversarial networks (GANs) är ett slags generativ modell som har fått mycket uppmärksamhet de senaste åren sedan de upptäcktes för sin potential att återskapa komplexa högdimensionella datafördelningar. Dessa förser en komprimerad representation av datan där enbart de karaktäriserande egenskaperna är bevarade, vilket följdaktligen inducerar ett avståndsmått på datarummet. Detta avståndsmått möjliggör interpolering inom datan vilket har åstadkommits med framgång tidigare. Häri föreslår vi en ny stokastisk interpoleringsmetod för GANs där interpolationen tvingas följa datafördelningen genom att implementera en sekventiell Monte Carlo algoritm för dragning av datapunkter. Resultaten för studien visar att metoden ger bättre interpolationer för datamängden LINES som användes; jämfört med resultaten av tidigare kända interpolationsmetoder syntes en märkbar förbättring genom kvalitativa och kvantitativa utvärderingar. Den framtagna interpolationsmetoden har alltså mött förväntningarna och är lovande, emellertid fordras att den testas på en mer komplex datamängd för att bekräfta att den fungerar väl även under mer generella förhållanden.
Barri, Alessandro. "Network mechanisms of memory storage in the balanced cortex." Thesis, Paris 5, 2014. http://www.theses.fr/2014PA05T060/document.
Повний текст джерелаIt is generally maintained that one of cortex’ functions is the storage of a large number of memories. In this picture, the physical substrate of memories is thought to be realised in pattern and strengths of synaptic connections among cortical neurons. Memory recall is associated with neuronal activity that is shaped by this connectivity. In this framework, active memories are represented by attractors in the space of neural activity. Electrical activity in cortical neurones in vivo exhibits prominent temporal irregularity. A standard way to account for this phenomenon is to postulate that recurrent synaptic excitation and inhibition as well as external inputs are balanced. In the common view, however, these balanced networks do not easily support the coexistence of multiple attractors. This is problematic in view of memory function. Recently, theoretical studies showed that balanced networks with synapses that exhibit short-term plasticity (STP) are able to maintain multiple stable states. In order to investigate whether experimentally obtained synaptic parameters are consistent with model predictions, we developed a new methodology that is capable to quantify both response variability and STP at the same synapse in an integrated and statistically-principled way. This approach yields higher parameter precision than standard procedures and allows for the use of more efficient stimulation protocols. However, the findings with respect to STP parameters do not allow to make conclusive statements about the validity of synaptic theories of balanced working memory. In the second part of this thesis an alternative theory of cortical memory storage is developed. The theory is based on the assumptions that memories are stored in attractor networks, and that memories are not represented by network states differing in their average activity levels, but by micro-states sharing the same global statistics. Different memories differ with respect to their spatial distributions of firing rates. From this the main result is derived: the balanced state is a necessary condition for extensive memory storage. Furthermore, we analytically calculate memory storage capacities of rate neurone networks. Remarkably, it can be shown that crucial properties of neuronal activity and physiology that are consistent with experimental observations are directly predicted by the theory if optimal memory storage capacity is required
Lýsek, Jiří. "Optimalizace síťového přepínače pomocí neuronové sítě." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2009. http://www.nusl.cz/ntk/nusl-228699.
Повний текст джерелаAckerman, Wesley. "Semantic-Driven Unsupervised Image-to-Image Translation for Distinct Image Domains." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8684.
Повний текст джерелаNord, Sofia. "Multivariate Time Series Data Generation using Generative Adversarial Networks : Generating Realistic Sensor Time Series Data of Vehicles with an Abnormal Behaviour using TimeGAN." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302644.
Повний текст джерелаNär man applicerar en modell för att utföra en maskininlärningsuppgift, till exempel att förutsäga utfall eller upptäcka avvikelser, är det viktigt med stora dataset för att uppnå hög prestanda, noggrannhet och generalisering. Det är dock inte ovanligt att dataset är små eller obalanserade eftersom insamling av data kan vara svårt, tidskrävande och dyrt. När man vill samla tidsserier från sensorer på fordon är dessa problem närvarande och de kan hindra bilindustrin i dess utveckling. Generering av syntetisk data har blivit ett växande intresse bland forskare inom flera områden som ett sätt att hantera problemen med datainsamling. Bland de metoder som undersökts för att generera data har generative adversarial networks (GANs) blivit ett populärt tillvägagångssätt i forskningsvärlden på grund av dess breda applikationsdomän och dess framgångsrika resultat. Denna avhandling fokuserar på att generera flerdimensionell tidsseriedata som liknar fordonssensoravläsningar av lufttryck i bromssystemet av fordon med onormalt beteende, vilket innebär att det finns ett läckage i systemet. En ny GAN modell kallad TimeGAN tränades för att genera sådan data och utvärderades sedan både kvalitativt och kvantitativt. Två versioner av denna modell testades och jämfördes. De erhållna resultaten visade att båda modellerna lärde sig distributionen och den underliggande informationen inom de olika signalerna i den verkliga datan. Målet med denna avhandling uppnåddes och kan lägga grunden för framtida arbete inom detta område.
Olsson, Jonathan. "Detecting Faulty Piles of Wood using Anomaly Detection Techniques." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-83061.
Повний текст джерелаShapero, Samuel Andre. "Configurable analog hardware for neuromorphic Bayesian inference and least-squares solutions." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/51719.
Повний текст джерелаHolub, Jiří. "Zvýšení kvality fotografie s použitím hlubokých neuronových sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2018. http://www.nusl.cz/ntk/nusl-377334.
Повний текст джерелаMattos, CÃsar Lincoln Cavalcante. "ComitÃs de Classificadores Baseados nas Redes SOM e Fuzzy ART com Sintonia de ParÃmetros e SeleÃÃo de Atributos via MetaheurÃsticas EvolucionÃrias." Universidade Federal do CearÃ, 2011. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=7034.
Повний текст джерелаO paradigma de classificaÃÃo baseada em comitÃs tem recebido considerÃvel atenÃÃo na literatura cientÃfica em anos recentes. Neste contexto, redes neurais supervisionadas tÃm sido a escolha mais comum para compor os classificadores base dos comitÃs. Esta dissertaÃÃo tem a intenÃÃo de projetar e avaliar comitÃs de classificadores obtidos atravÃs de modificaÃÃes impostas a algoritmos de aprendizado nÃo-supervisionado, tais como as redes Fuzzy ART e SOM, dando origem, respectivamente, Ãs arquiteturas ARTIE (ART in Ensembles) e MUSCLE (Multiple SOM Classifiers in Ensembles). A sintonia dos parÃmetros e a seleÃÃo dos atributos das redes neurais que compÃem as arquiteturas ARTIE e MUSCLE foram tratados por otimizaÃÃo metaheurÃstica, a partir da proposiÃÃo do algoritmo I-HPSO (Improved Hybrid Particles Swarm Optimization). As arquiteturas ARTIE e MUSCLE foram avaliadas e comparadas com comitÃs baseados nas redes Fuzzy ARTMAP, LVQ e ELM em 12 conjuntos de dados reais. Os resultados obtidos indicam que as arquiteturas propostas apresentam desempenhos superiores aos dos comitÃs baseados em redes neurais supervisionadas.
Marek, Jan. "Rekonstrukce chybějících části obličeje pomocí neuronové sítě." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2020. http://www.nusl.cz/ntk/nusl-433506.
Повний текст джерелаMassaccesi, Luciano. "Machine Learning Software for Automated Satellite Telemetry Monitoring." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20502/.
Повний текст джерелаBak, Adam. "Simulace projevu kožního onemocnění s využitím GAN." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445569.
Повний текст джерелаAntipov, Grigory. "Apprentissage profond pour la description sémantique des traits visuels humains." Thesis, Paris, ENST, 2017. http://www.theses.fr/2017ENST0071/document.
Повний текст джерелаThe recent progress in artificial neural networks (rebranded as deep learning) has significantly boosted the state-of-the-art in numerous domains of computer vision. In this PhD study, we explore how deep learning techniques can help in the analysis of gender and age from a human face. In particular, two complementary problem settings are considered: (1) gender/age prediction from given face images, and (2) synthesis and editing of human faces with the required gender/age attributes.Firstly, we conduct a comprehensive study which results in an empirical formulation of a set of principles for optimal design and training of gender recognition and age estimation Convolutional Neural Networks (CNNs). As a result, we obtain the state-of-the-art CNNs for gender/age prediction according to the three most popular benchmarks, and win an international competition on apparent age estimation. On a very challenging internal dataset, our best models reach 98.7% of gender classification accuracy and an average age estimation error of 4.26 years.In order to address the problem of synthesis and editing of human faces, we design and train GA-cGAN, the first Generative Adversarial Network (GAN) which can generate synthetic faces of high visual fidelity within required gender and age categories. Moreover, we propose a novel method which allows employing GA-cGAN for gender swapping and aging/rejuvenation without losing the original identity in synthetic faces. Finally, in order to show the practical interest of the designed face editing method, we apply it to improve the accuracy of an off-the-shelf face verification software in a cross-age evaluation scenario
Hubený, Marek. "Koncepty strojového učení pro kategorizaci objektů v obrazu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2017. http://www.nusl.cz/ntk/nusl-316388.
Повний текст джерелаWei, Wen. "Apprentissage automatique des altérations cérébrales causées par la sclérose en plaques en neuro-imagerie multimodale." Thesis, Université Côte d'Azur, 2020. http://www.theses.fr/2020COAZ4021.
Повний текст джерелаMultiple Sclerosis (MS) is the most common progressive neurological disease of young adults worldwide and thus represents a major public health issue with about 90,000 patients in France and more than 500,000 people affected with MS in Europe. In order to optimize treatments, it is essential to be able to measure and track brain alterations in MS patients. In fact, MS is a multi-faceted disease which involves different types of alterations, such as myelin damage and repair. Under this observation, multimodal neuroimaging are needed to fully characterize the disease. Magnetic resonance imaging (MRI) has emerged as a fundamental imaging biomarker for multiple sclerosis because of its high sensitivity to reveal macroscopic tissue abnormalities in patients with MS. Conventional MR scanning provides a direct way to detect MS lesions and their changes, and plays a dominant role in the diagnostic criteria of MS. Moreover, positron emission tomography (PET) imaging, an alternative imaging modality, can provide functional information and detect target tissue changes at the cellular and molecular level by using various radiotracers. For example, by using the radiotracer [11C]PIB, PET allows a direct pathological measure of myelin alteration. However, in clinical settings, not all the modalities are available because of various reasons. In this thesis, we therefore focus on learning and predicting missing-modality-derived brain alterations in MS from multimodal neuroimaging data
Prokopetc, Kristina. "Precise Mapping for Retinal Photocoagulation in SLIM (Slit-Lamp Image Mosaicing)." Thesis, Université Clermont Auvergne (2017-2020), 2017. http://www.theses.fr/2017CLFAC093/document.
Повний текст джерелаThis thesis arises from an agreement Convention Industrielle de Formation par la REcherche (CIFRE) between the Endoscopy and Computer Vision (EnCoV) research group at Université Clermont Auvergne and the company Quantel Medical (www.quantel-medical.fr), which specializes in the development of innovative ultrasound and laser products in ophthalmology. It presents a research work directed at the application of computer-aided diagnosis and treatment of retinal diseases with a use of the TrackScan industrial prototype developed at Quantel Medical. More specifically, it contributes to the problem of precise Slit-Lamp Image Mosaicing (SLIM) and automatic multi-modal registration of SLIM with Fluorescein Angiography (FA) to assist navigated pan-retinal photocoagulation. We address three different problems.The first is a problem of accumulated registration errors in SLIM, namely the mosaicing drift.A common approach to image mosaicking is to compute transformations only between temporally consecutive images in a sequence and then to combine them to obtain the transformation between non-temporally consecutive views. Many existing algorithms follow this approach. Despite the low computational cost and the simplicity of such methods, due to its ‘chaining’ nature, alignment errors tend to accumulate, causing images to drift in the mosaic. We propose to use recent advances in key-frame Bundle Adjustment methods and present a drift reduction framework that is specifically designed for SLIM. We also introduce a new local refinement procedure.Secondly, we tackle the problem of various types of light-related imaging artifacts common in SLIM, which significantly degrade the geometric and photometric quality of the mosaic. Existing solutions manage to deal with strong glares which corrupt the retinal content entirely while leaving aside the correction of semi-transparent specular highlights and lens flare. This introduces ghosting and information loss. Moreover, related generic methods do not produce satisfactory results in SLIM. Therefore, we propose a better alternative by designing a method based on a fast single-image technique to remove glares and the notion of the type of semi-transparent specular highlights and motion cues for intelligent correction of lens flare.Finally, we solve the problem of automatic multi-modal registration of FA and SLIM. There exist a number of related works on multi-modal registration of various retinal image modalities. However, the majority of existing methods require a detection of feature points in both image modalities. This is a very difficult task for SLIM and FA. These methods do not account for the accurate registration in macula area - the priority landmark. Moreover, none has developed a fully automatic solution for SLIM and FA. In this thesis, we propose the first method that is able to register these two modalities without manual input by detecting retinal features only on one image and ensures an accurate registration in the macula area.The description of the extensive experiments that were used to demonstrate the effectiveness of each of the proposed methods is also provided. Our results show that (i) using our new local refinement procedure for drift reduction significantly ameliorates the to drift reduction allowing us to achieve an improvement in precision over the current solution employed in the TrackScan; (ii) the proposed methodology for correction of light-related artifacts exhibits a good efficiency, significantly outperforming related works in SLIM; and (iii) despite our solution for multi-modal registration builds on existing methods, with the various specific modifications made, it is fully automatic, effective and improves the baseline registration method currently used on the TrackScan
Mamba, Mpendulo, and Mpendulo Mamba. "Automatic Brain Tumor Segmentation with a 3-Dimensional Generative Adversarial Neural Network." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/4m9m82.
Повний текст джерела國立臺北科技大學
電資國際專班
106
Brain tumor segmentation is a very crucial task in medical image processing. Early diagnosis of brain tumors plays an important role in improving treatment possibilities and increases the survival rate of the patients. Manual segmentation of the brain tumors for cancer diagnosis, from large amounts of magnetic resonance images (MRI) generated in clinical routine, is a difficult and time consuming task. There is a need for automatic brain image segmentation. In this work, we demonstrate a deep neural network for volumetric segmentation that learns from a series of annotated volumetric images given in the Neuroimaging Informatics Technology Initiative (NIfTI) format. Recently, automatic segmentation using deep learning methods proved effective since these methods achieve state-of-the-art results and can address the problem better than other methods. Deep learning methods can also enable efficient processing and objective evaluation of the large amounts of MRI-based images. We investigate 3D conditional adversarial networks as a novel solution to 3D image segmentation for medical segmentation problems. These networks not only learn the mapping from input images to output images, but also learn a loss function to train the mapping between them. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We show that this method is effective at generating slices of segmentation data from 3D labelled maps. We utilize a dataset from the medical image computing and computer assisted intervention (MICCAI), which consists of MRI scans of high-grade gliomas (HGG) which are tumors of the central nervous system and low-grade gliomas (LGG) which are referred to as slow-growing tumors. The proposed model is able to discriminate between well segmented and poorly segmented images and the generative model can create segmentation image masks around the tumors and achieves an 80.57% dice score when compared with the dataset.
Wu, Meng-Xiu, and 吳孟修. "Image Compression Based on Fuzzy Competitive Learning Neural Network." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/88469142545554927818.
Повний текст джерела國立成功大學
工程科學系碩博士班
91
A novel image compression algorithm using fuzzy competitive learning neural network is presented in this thesis. The proposed image compression scheme is based on vector quantization. Then, competitive learning neural network and fuzzy control system are included in this scheme. It modifies the learning rate and scaling function of updating equation, which is used to train the codebook, with competitive learning neural network and fuzzy control system, respectively. In the proposed scheme, mean-square error and rate of mean-square error are the inputs of the fuzzy control system, using the membership function and control rules to design the codebook instantaneously and encode the source image in the meanwhile. The monochrome-CCD camera and image acquisition board of PCI interference are used to demo the proposed scheme. According to the experimental results, our scheme could greatly improve the quality of codebook. And comparing with conventional vector quantization, taking the 1024 4 codebook size for example, about 10 percentage of PSNR (peak signal-to-noise ratio) is increased in experiments.
Yen, Chang Kun, and 張崑淵. "The Research and Application of Competitive Hopfield Neural Network." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/98064017695098755053.
Повний текст джерела大葉大學
電機工程學系碩士在職專班
94
Radar system plays an important role in both defense industries and civil applications. In order to obtain the performance radar system should have good tracking algorithm, therefore, it can obtain high detection probability and reduce the tracking errors. A neural network model is investigated in this thesis. Such a mathematical model applying Hopfield Neural Network to tracking systems will have more accurate tracking results. The proposed tracking procedure is developed in this thesis. Moreover, one simulation program using Matlab is also designed. According to the simulation results, this tracking algorithm have good performance.
Yang, Shr-shian, and 楊士賢. "Chaotic Competitive Hopfield Neural Network for Medical Image Segmentation." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/91334635333622287045.
Повний текст джерела國立雲林科技大學
電子工程與資訊工程技術研究所
88
In this thesis, we use the dynamic of chaotic neural network and map into Hopfield model for medical image segmentation. From chaotic dynamic and Hopfield model structure, we could let wining unit which falls into local stable state by winner-take-all rule escape original local stable state, and increase the probability of falling into global stable state. In the whole structure, we introduce a 2-D chaotic Hopfield neural network and an unsupervised competitive Hopfield neural network to parallel process for medical MR image. In medical image segmentation, we are according to global gray distribution and Hopfield neural network model, so we could let chaotic competitive Hopfield neural network algorithm reach an optimum solution and the optimum solution which is based on Lyapunov energy function. When Lyapunov energy function converges to a stable state, we can get an optimum solution. In algorithm, we proposed two algorithms, 1) According to probability of chaotic dynamic, 2) According to Lyapunov energy function convergence. These two algorithms have different advantages and disadvantages but their algorithm result is better than the existing of algorithms of Hard-C Means, Fuzzy-C Means and CHNN.
CHUANG, FU-CHEN, and 莊馥甄. "Use the Generative Adversarial Network and Attention Model to customize the Neural Style." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/45nr69.
Повний текст джерела東吳大學
數學系
107
With the advancement of the information age, the photo app combined with artificial intelligence in the painting method is more and more popular, and the style conversion characteristics are more and more diversified. In the past,there was little use of abstract art. So the purpose of this research is to create novel abstract perception art images, which is to create a new abstract style. It belongs to the artist's unique art, but at the same time it blends with the pictures of the real world.First, using the creative adversarial networks model to create novel abstract art images. Second, using convolutional neural networks and convolutional block attention module to convert images into high-level features and low-resolution images. And then generated into a high-quality image through the perceptual loss network, producing a perceptual art images, showing the emergence of real-world features, and finally combining these models into a novel abstract perception art images. With this model, it is easier to create your own. .
Po-JungTsai and 蔡帛融. "Using generative adversarial learning to enhance the attention transfer in convolutional neural network." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/87e4j8.
Повний текст джерелаCHENG, HAO-WEN, and 鄭豪文. "Chip and System Design of Image Enhancement Based on Generative Adversarial Neural Network." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/r2dve4.
Повний текст джерела國立臺北科技大學
電子工程系
107
Visual image processing has always been a very important field. With the development of multimedia, we can use images in everywhere. The quality of images is not perfect in our expectation. Therefore, image processing is to carry out the images to be repaired. Not only enhance image of contrast to make clear, but real-time processing is also very important. For example, the vehicle electronic assistant equipment must perform image restoration in a short time, so that the driver can keep safety with enhanced images. Due to the development of deep learning in recent years, we can use the neural network for training and simulating the models. The machine learning method can replace the traditional method, which not only shortens the time but also has higher precision. We use deep learning to perform image decomposition, so that we can get the shadow image and the reflection image, and then do the enhancement for the shadow layer image. That can be faster than the traditional methods and perform better efficient. It is possible to fix the over-exposed or over-dark parts of the image to get more complete information, and the shadow layer repair we also use the neural network by algorithm simulation, the conditional generation can be used to restrict the network. Let the speed of training and testing be more precise and fast. Finally, we make the chip to against the most computational volume of the mathematical formula - convolution, and the overall speed can be improved by the hardware acceleration.
(11211114), Qingyi Gao. "ADVERSARIAL LEARNING ON ROBUSTNESS AND GENERATIVE MODELS." Thesis, 2021.
Знайти повний текст джерелаSusskind, Joshua Matthew. "Interpreting Faces with Neurally Inspired Generative Models." Thesis, 2011. http://hdl.handle.net/1807/29884.
Повний текст джерелаPeng, Chung-Yun, and 彭中鋆. "A Novel Harmonic Competitive Neural Network─Applied to VQ, Clustering and Classification." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/90209094200523147361.
Повний текст джерела國立海洋大學
電機工程學系
87
This thesis presents a harmonic on-line learning algorithm useful for training self-creating and self-organizing competitive neural networks. The resulting network is called Harmonic Competitive Neural Network (HCNN). It is shown that, by employing dual local resource counters to record the activity of each node during the competitive learning process, the equi-error and equi-probable criteria can be coherently harmonized. Training in HCNN is smooth and incremental, it not only achieves the biologically plausible on-line learning property, but it also avoids the stability-and-plasticity dilemma, the dead-node problem, as well as the deficiency of local minimum. Vector quantization, clustering and classification are essential techniques in image processing and pattern recognition. We apply the HCNN to perform the three important tasks. In vector quantization, the proposed HCNN is very effective in on-line learning vector quantization. Comparison studies on learning vector quantization involving stationary and non-stationary, structured and non-structured inputs demonstrate that the HCNN outperforms other competitive networks in terms of quantization error, training speed and harmonization of MSE and entropy. Augmented with an agglomerating algorithm, the HCNN can be easily tailored for clustering tasks. Unlike the k-means algorithm and the MST clustering method, the proposed HCNN-based clustering scheme is fully autonomous in that the number of clusters needs not be given in advance, and it consumes less computation time. Finally, we applied HCNN to learning classification. Tested with the two-spiral and iris data, simulation results have shown that HCNN is capable of performing accurate classification.
Ruiz, Vito Manuel. "Adaptation in a deep network." Thesis, 2011. http://hdl.handle.net/2152/ETD-UT-2011-05-3156.
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Cheng, Tzu-Kuan, and 鄭子寬. "Neural Mechanism of Attention Network under Competitive Stress Using Simultaneous EEG & fNIRS Recording." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/18117195361184728457.
Повний текст джерела國立交通大學
電控工程研究所
104
Understanding the dynamics of brain activity during social interaction is important for understanding our social nature and in turn for improving the life quality of people. However, two important limitations of the existing studies are evident. First, social interaction involves two or more individuals. Second, social interaction in real life occurs in a naturalistic environment. We used EEG/fNIRS simultaneous caps with modified attention network task to explore the hemodynamic and metabolic changes associated with EEG-recorded changes in neuronal activity. There are two aims of this study. First, using attention network task to find the cognitive mechanism of human brain under competitive pressure. Second, we want to know whether different competition results have different phenomenon in two physiological signals. Use data in competition minus in non-competition (normal) and find the influence of competitive pressure. In competitive pressure, ERSP in left frontal has theta band burst before response. Oxygenated hemoglobin (HbO) in competition task is lower and raise slower than normal task. In the comparison of win and lose, ERSP have no different because participants all in a very attention status, thus their ERSP result from event onset to response offset have no significant difference. HbO in win group is higher than lose task. The electrodynamic and hemodynamic signatures of competitive pressure provided in this study might improve the understanding of the neural mechanism of stress.
Roussakov, Maxime. "Financial time series analysis with competitive neural networks." Thèse, 2017. http://hdl.handle.net/1866/20210.
Повний текст джерела(8892395), Yao Chen. "Inferential GANs and Deep Feature Selection with Applications." Thesis, 2020.
Знайти повний текст джерелаHonzátko, David. "Využití generativních modelů neuronových sítí v obrazové rekonstrukci." Master's thesis, 2018. http://www.nusl.cz/ntk/nusl-372863.
Повний текст джерелаParracho, João Oliveira. "JOINT CODING OF MULTIMODAL BIOMEDICAL IMAGES US ING CONVOLUTIONAL NEURAL NETWORKS." Master's thesis, 2020. http://hdl.handle.net/10400.8/6682.
Повний текст джерелаRodrigues, Diogo Manuel de Castro. "Integrating Vision and Language for Automatic Face Descriptions." Master's thesis, 2018. http://hdl.handle.net/10316/86752.
Повний текст джерелаNesta dissertação, para criar um exemplo único de um sistema de face para texto e texto para face foi integrado visão por computador e processamento de linguagem natural. O propósito é fornecer uma solução que permita ajudar os seres humanos a realizar funções com maior qualidade e de forma mais rápida. Assim sendo pretende-se criar um sistema que possa ser usado, por exemplo, para descrever rostos para pessoas com deficiência visual ou para gerar rostos a partir de descrições para investigações criminais. No entanto trata-se apenas de uma versão preliminar, na medida em que o curto tempo disponível para a sua realização não permitiu alcançar a ambiciosa proposta. De forma a atingir este objectivo, foi criado um sistema com a capacidade de descrever textualmente imagens faciais e por outro lado, gerar automaticamente imagens faciais a partir de descrições textuais. O sistema é dividido em duas partes, a primeira tem como função prever atributos das imagens faciais através de uma rede neuronal convolucional. Estes são utilizados como base para o modelo de geração de linguagem natural, gerando descrições textuais numa metodologia baseada em regras. A segunda parte, usa uma técnica simples de extração de palavras chave para analisar o texto e identificar os atributos nessa descrição. Seguidamente, o sistema usa uma rede generativa adversarial para gerar uma imagem facial com o conjunto das características desejadas. Os atributos são usados como base no nosso método, uma vez que representam um identificador dominante que transmite características sobre um rosto com eficácia.Os resultados demonstraram, mais uma vez, que os métodos CNN e GAN são atualmente as melhores opções para, tarefas de reconhecimento e geração de imagens, respectivamente. Esta conclusão destá assente nos resultados convincentes. Por outro lado, os métodos de processamento de linguagem natural apesar de terem funcionado bem, de acordo com os objectivos, os seus resultados são menos notáveis, especialmente o modelo de geração de linguagem natural. Este trabalho propõe uma solução fiável e funcional para resolver este sistema complexo, no entanto é uma área que merece uma extensa investigação e desenvolvimento.
In this dissertation, computer vision and Natural Language Processing (NLP) are integrated to create a unique example of a face-to-text and text-to-face system. Its intention is to provide a solution that can help humans to perform their jobs with better quality and with a quick response. The aim is to create a system that can be used, for example, to describe faces for visually impaired people or to generate faces from descriptions for criminal investigations. However, this is a preliminary version as it is an ambitious goal to be achieved during the time available for its realization.To accomplish this motivation, a system was created with the capability of describing, textually, facial images, along with the ability to automatically generate face images from text descriptions. The system is divided into two sub-systems. The first part predicts attributes from the face images through a Convolutional Neural Network (CNN) method that are used, further, as a base to the Natural Language Generation (NLG) model. The descriptions are generated on a rule-based methodology. The second part of the system uses a simple keyword extraction technique to analyze the text and identify the attributes on that description. After that, it uses a conditional Generative Adversarial Network (GAN) to generate a facial image with a specific set of desired attributes. The reason why attributes are used as a base on the method is because they are a dominant identifier that can efficiently transmit characteristic about a face. The results demonstrate, once again, that either CNN and GAN methods are presently the best options for recognition and generation tasks, respectively. This conclusion is due to their convincing results. On the other hand, the NLP methods worked well for their purposes. However, its results are less remarkable, especially the NLG model. This work proposes a reliable and functional solution for solving this complex system. Nevertheless, this area needs an extensive investigation and development.
Dumoulin, Vincent. "Representation Learning for Visual Data." Thèse, 2018. http://hdl.handle.net/1866/21140.
Повний текст джерелаWarde-Farley, David. "Feedforward deep architectures for classification and synthesis." Thèse, 2017. http://hdl.handle.net/1866/20501.
Повний текст джерелаSantos, Ângelo Emanuel Neves dos. "Design and simulation of a smart bottle with fill-level sensing based on oxide TFT technology." Master's thesis, 2016. http://hdl.handle.net/10362/19593.
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