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Improving Bots Playing Starcraft II Game in PySC2 Environment
Krušina, Jan ; Škoda, Petr (referee) ; Smrž, Pavel (advisor)
The aim of this thesis is to create an automated system for playing a real-time strategy game Starcraft II. Learning from replays via supervised learning and reinforcement learning techniques are used for improving bot's behavior. The proposed system should be capable of playing the whole game utilizing PySC2 framework for machine learning. Performance of the bot is evaluated against the built-in scripted AI in the game.
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Deep Neural Networks for Classifying Objects in an Image
Mlynarič, Tomáš ; Zemčík, Pavel (referee) ; Hradiš, Michal (advisor)
This paper deals with classifying objects using deep neural networks. Whole scene segmentation was used as main algorithm for the classification purpose which works with video sequences and obtains information between two video frames. Optical flow was used for getting information from the video frames, based on which features maps of a~neural network are warped. Two neural network architectures were adjusted to work with videos and experimented with. Results of the experiments show, that using videos for image segmentation improves accuracy (IoU) compared to the same architecture working with images.
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Deep Learning for Facial Recognition in Video
Stratil, Jan ; Sochor, Jakub (referee) ; Hradiš, Michal (advisor)
This bachelor's thesis deals with facial recognition in video using deep neural networks. This task is split into 2 parts. The first part deals with training network that produces compact feature vector which represents the face identity from a video frame. The second part deals with training aggregation network that aggregates those feature vectors into one. This aggregation is fast and it has shown that its results are better than naive pooling methods. Results are tested on the LFW dataset, where it achieves 92.8% accuracy and on the YTF dataset, where the accuracy is 84.06%.
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Reinforcement Learning for RoboCup
Bočán, Hynek ; Škoda, Petr (referee) ; Smrž, Pavel (advisor)
Goal of this thesis is creation of artificial intelligence capable of controlling robotic soccer player simulated in SimSpark environment. Agent created is expanding capabilities of existing third party agent which provides set of basic skills such as localization on the field, dribbling with the ball and omnidirectional walk. Responsibility of the created agent is to pick the best action based current state of the game. This decision making was implemented using reinforcement learning and its method Q-learning. State of the game is transformed into 2D picture with several planes. This picture is then analyzed using deep convolution neural network implemented using C++ and DeepCL library.
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Video Enhancement Using Convolutional Networks
Skácel, David ; Špaňhel, Jakub (referee) ; Hradiš, Michal (advisor)
Convolutional neural networks (CNN) represent a state-of-the-art approach to non-trivial image processing tasks, including compression artifacts reduction and image super-resolution. As some research groups nowadays show, these networks can also be leveraged to perform such tasks on real-world video data, resulting in video spatial super-resolution and more. The main goal of this work is to determine whether these nets can be adjusted to perform temporal super-resolution of real-world video data. I utilize the aforementioned neural net architectures in this paper to do so. As I show, given that the input videos are of reasonable quality, these nets are capable of double-image interpolation up to a certain level, where the output image is usable for temporal upsampling. Although the presented results are promising, I encourage more research to be done on this topic.
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Visual Car-Detection on the Parking Lots Using Deep Neural Networks
Stránský, Václav ; Veľas, Martin (referee) ; Rozman, Jaroslav (advisor)
The concept of smart cities is inherently connected with efficient parking solutions based on the knowledge of individual parking space occupancy. The subject of this paper is the design and implementation of a robust system for analyzing parking space occupancy from a multi-camera system with the possibility of visual overlap between cameras. The system is designed and implemented in Robot Operating System (ROS) and its core consists of two separate classifiers. The more successful, however, a slower option is detection by a deep neural network. A quick interaction is provided by a less accurate classifier of movement with a background model. The system is capable of working in real time on a graphic card as well as on a processor. The success rate of the system on a testing data set from real operation exceeds 95 %.
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Deep Neural Networks for Sales Forecasting
Tyrpáková, Natália ; Pilát, Martin (advisor) ; Mrázová, Iveta (referee)
Sales forecasting is an essential part of supply chain management. In retail business, accurate sales forecasts lead to significant cost reductions. Statistical methods that are commonly used for sales forecasting often overlook important aspects unique for the sales time series, which lowers the forecast accuracy. In this thesis we explore whether it is possible to improve short-term sales forecasting by employing deep neural networks. This thesis analyzes performance of various traditional deep neural network designs and proposes a novel architecture. It also explores several data preprocessing methods, both traditional and non-traditional, which turns out to be a crucial part of sales forecasting using deep neural networks. The best methods of deep neural network approach that we found are then compared to other forecasting methods such as traditional neural networks or exponential smoothing. Powered by TCPDF (www.tcpdf.org)
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Car Licence Plate Anonymization
Skřivánková, Barbora ; Zachariášová, Marcela (referee) ; Drahošová, Michaela (advisor)
While browsing an online map server, continuous photographs of certain places can be browsed as well. When the map service takes pictures of a public space, there are some personal data captured as well (i.e. faces, car licence plates). The goal of this thesis is the design of automated car licence plates anonymization system, optimized for the Panorama service provided by the Seznam.cz a.s. corporation. In this thesis, the process of car licence plate anonymization is divided into two parts: the first one solves a detection of cars and the second solves a car licence plate localization in the selected image. The car detection is based on the deep neural network approach, the car licence plate localization is solved by using a fully connected neural network performing a regression task. The goal of this thesis is to get over the disadvantages of commercial solution used nowadays. These are false posititive results and high computational complexity. Results of this thesis are not as good as expected. The reason could be a dataset provided by Seznam.cz a.s. corporation, which seemed to be robust enough in the beginning, but in the end it showed up to be not suffice enough to train the neural network.
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Multi-Task Neural Networks for Speech Recognition
Egorova, Ekaterina ; Veselý, Karel (referee) ; Karafiát, Martin (advisor)
První část této diplomové práci se zabývá teoretickým rozborem principů neuronových sítí, včetně možnosti jejich použití v oblasti rozpoznávání řeči. Práce pokračuje popisem viceúkolových neuronových sítí a souvisejících experimentů. Praktická část práce obsahovala změny software pro trénování neuronových sítí, které umožnily viceúkolové trénování. Je rovněž popsáno připravené prostředí, včetně několika dedikovaných skriptů. Experimenty představené v této diplomové práci ověřují použití artikulačních characteristik řeči pro viceúkolové trénování. Experimenty byly provedeny na dvou řečových databázích lišících se kvalitou a velikostí a representujících různé jazyky - angličtinu a vietnamštinu. Artikulační charakteristiky byly také kombinovány s jinými sekundárními úkoly, například kontextem, s záměrem ověřit jejich komplementaritu. Porovnaní je provedeno s neuronovými sítěmi různých velikostí tak, aby byl popsán vztah mezi velikostí neuronových sítí a efektivitou viceúkolového trénování. Závěrem provedených experimentů je, že viceúkolové trénování s použitím artikulačnich charakteristik jako sekundárních úkolů vede k lepšímu trénování neuronových sítí a výsledkem tohoto trénování může být přesnější rozpoznávání fonémů. V závěru práce jsou viceúkolové neuronové sítě testovány v systému rozpoznávání řeči jako extraktor příznaků.
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Deep Neural Networks
Habrnál, Matěj ; Zbořil, František (referee) ; Zbořil, František (advisor)
The thesis addresses the topic of Deep Neural Networks, in particular the methods regar- ding the field of Deep Learning, which is used to initialize the weight and learning process s itself within Deep Neural Networks. The focus is also put to the basic theory of the classical Neural Networks, which is important to comprehensive understanding of the issue. The aim of this work is to determine the optimal set of optional parameters of the algori- thms on various complexity levels of image recognition tasks through experimenting with created application applying Deep Neural Networks. Furthermore, evaluation and analysis of the results and lessons learned from the experimentation with classical and Deep Neural Networks are integrated in the thesis.
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