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Low-Dimensional Matrix Factorization in End-To-End Speech Recognition Systems
Gajdár, Matúš ; Grézl, František (referee) ; Karafiát, Martin (advisor)
The project covers automatic speech recognition with neural network training using low-dimensional matrix factorization. We are describing time delay neural networks with factorization (TDNN-F) and without it (TDNN) in Pytorch language. We are comparing the implementation between Pytorch and Kaldi toolkit, where we achieve similar results during experiments with various network architectures. The last chapter describes the impact of a low-dimensional matrix factorization on End-to-End speech recognition systems and also a modification of the system with TDNN(-F) networks. Using specific network settings, we were able to achieve better results with systems using factorization. Additionally, we reduced the complexity of training by decreasing network parameters with the use of TDNN(-F) networks.
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Cell detection using convolutional neural networks
Doskočil, Ondřej ; Chmelík, Jiří (referee) ; Vičar, Tomáš (advisor)
This bachelor thesis deals with the use of convolutional neural networks for cell detection in image data. The theoretical part contains a description of the functioning of these networks and their various architectures. In the practical part, these networks were implemented and trained on an available dataset. However, each of these networks uses a different approach to detection. Finally, the individual networks were statistically evaluated and a discussion was conducted.
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Detection of persons and evaluation of gender and age in image data
Dobiš, Lukáš ; Vičar, Tomáš (referee) ; Kolář, Radim (advisor)
Táto diplomová práca sa venuje automatickému rozpoznávaniu ludí v obrazových dátach s využitím konvolučných neurónových sieti na určenie polohy tváre a následnej analýze získaných dát. Výsledkom analýzy tváre je určenie pohlavia, emócie a veku osoby. Práca obsahuje popis použitých architektúr konvolučných sietí pre každú podúlohu. Sieť na odhad veku má natrénované nové váhy, ktoré sú vzápätí zmrazené a majú do svojej architektúry vložené LSTM vrstvy. Tieto vrstvy sú samostatne dotrénované a testované na novom datasete vytvorenom pre tento účel. Výsledky testov ukazujú zlepšenie predikcie veku. Riešenie pre rýchlu, robustnú a modulárnu detekciu tváre a ďalších ludských rysov z jedného obrazu alebo videa je prezentované ako kombinácia prepojených konvolučných sietí. Tieto sú implementované v podobe skriptu a následne vysvetlené. Ich rýchlosť je dostatočná pre ďalšie dodatočné analýzy tváre na živých obrazových dátach.
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Obtaining and Processing of a Set of Vehicle License Plates
Kvapilová, Aneta ; Bartl, Vojtěch (referee) ; Herout, Adam (advisor)
This master thesis focuses on creating and processing a dataset, which contains semi-automatically processed images of vehicles licence plates. The main goal is to create videos and a set of tools, which are able to transform input videos into a dataset used for traffic monitoring neural networks. Used programming language is Python, graphical library OpenCV and framework PyTorch for implementation of neural network.
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Convolutional Networks for Lip Reading
Kadleček, Josef ; Kišš, Martin (referee) ; Hradiš, Michal (advisor)
This thesis deals with current methods for automatic speech recognition and lip reading via neural networks. Furthermore it deals with similarities in the architectures of neural networks for audio and visual data and available datasets in the field of audiovisual automatic speech recognition. The main contribution of this thesis is set of experiments comparing different changes in neural network architecture and its impact on results. The thesis includes an implementation of a system for automatic speech recognition from audio (CER: 12.6 %) and visual (CER: 57,7 %) data. The architectures of both systems are based on features extraction via convolutional networks followed by recurrent layers LSTM, another layer of convolutions and loss function CTC.
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Computer vision and hand gestures detection and fingers tracking
Bravenec, Tomáš ; Wyrzykowski, Roman (referee) ; Frýza, Tomáš (advisor)
Diplomová práce je zaměřena na detekci a rozpoznání gest rukou a prstů ve statických obrazech i video sekvencích. Práce obsahuje shrnutí několika různých přístupů k samotné detekci a také jejich výhody i nevýhody. V práci je též obsažena realizace multiplatformní aplikace napsané v Pythonu s použitím knihoven OpenCV a PyTorch, která dokáže zobrazit vybraný obraz nebo přehrát video se zvýrazněním rozpoznaných gest.
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Reinforcement Learning for 3D Games
Beránek, Michal ; Herout, Adam (referee) ; Hradiš, Michal (advisor)
Thesis deals with neural network learning on simple tasks in 3D shooter Doom, mediated by research platform ViZDoom. The main goal is to create an agent, which is able to learn multiple tasks simultaneously. Reinforcement learning algorithm used to achieve this goal is called Rainbow, which combines several improvements of DQN algorithm. I proposed and experimented with two different architectures of neural network for learning multiple tasks. One of them was successful and after a relatively short period of learning it reached almost 50% of maximum possible reward. The key element of this achievement is an Embedding layer for parametric description of task environment. The main discovery is, that Rainbow is able to learn in 3D environment and with the help of Embedding layer, it is able to learn on multiple tasks simultaneously.
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Neural Networks for Network Anomaly Detection
Matisko, Maroš ; Martinásek, Zdeněk (referee) ; Blažek, Petr (advisor)
This bachelor thesis is focused on creating a system to mitigate computer network attacks. One of the most common groups of attacks is Distributed Denial of Service (DDoS) attacks, against which this system should protect internal network. In the theoretical part of the thesis are described DDoS attacks, existing systems for their mitigations, neural networks principle and their use. Practical part consists of choosing communication parameters, constructing a neural network with use of these parameters, implementation of this neural network in real–time attack mitigation system and a result of testing of this system.
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