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Deep Neural Networks Approximation
Stodůlka, Martin ; Mrázek, Vojtěch (referee) ; Vaverka, Filip (advisor)
The goal of this work is to find out the impact of approximated computing on accuracy of deep neural network, specifically neural networks for image classification. A version of framework Caffe called Ristretto-caffe was chosen for neural network implementation, which was extended for the use of approximated operations. Approximated computing was used for multiplication in forward pass for convolution. Approximated components from Evoapproxlib were chosen for this work.
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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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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 Learning for Image Recognition
Munzar, Milan ; Kolář, Martin (referee) ; Hradiš, Michal (advisor)
Neural networks are one of the state-of-the-art models for machine learning today. One may found them in autonomous robot systems, object and speech recognition, prediction and many others AI tasks. The thesis describes this model and its extension which is used in an object recognition. Then explains an application of a convolutional neural networks(CNNs) in an image recognition on Caltech101 and Cifar10 datasets. Using this exemplar application, the thesis discusses and measures efficiency of techniques used in CNNs. Results show that the convolutional networks without advanced extensions are able to reach a 80\% recognition accuracy on Cifar-10 and a 37\% accuracy on Caltech101.
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Image data segmentation using deep neural networks
Hrdý, Martin ; Myška, Vojtěch (referee) ; Kiac, Martin (advisor)
The main aim of this master’s thesis is to get acquainted with the theory of the current segmentation methods, that use deep learning. Segmentation neural network that will be capable of segmenting individual instances of the objects will be proposed and created based on theoretical knowledge. The main focus of the segmentation neural network will be segmentation of electronic components from printed circuit boards.
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Deep Neural Networks for Defect Detection
Juřica, Tomáš ; Herout, Adam (referee) ; Hradiš, Michal (advisor)
The goal of this work is to bring automatic defect detection to the manufacturing process of plastic cards. A card is considered defective when it is contaminated with a dust particle or a hair. The main challenges I am facing to accomplish this task are a very few training data samples (214 images), small area of target defects in context of an entire card (average defect area is 0.0068 \% of the card) and also very complex background the detection task is performed on. In order to accomplish the task, I decided to use Mask R-CNN detection algorithm combined with augmentation techniques such as synthetic dataset generation. I trained the model on the synthetic dataset consisting of 20 000 images. This way I was able to create a model performing 0.83 AP at 0.1 IoU on the original data test set.
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Detector of the Human Head in Image
Svoboda, Jakub ; Orság, Filip (referee) ; Goldmann, Tomáš (advisor)
Detection of human head is an important part of person detection and identification algorithms. This thesis is focused on the detection of human head with methods based on neural networks. The majority the of conventional detectors can identify objects within a limited range of positions, whereas models based on neural networks offer a more robust approach. In this thesis we trained the current state-of-the-art models and compared their accuracy and speed. The most accurate model proved to be RetinaNet which has reached 85.15% AP. This detector can be used to improve current available algorithms for person detection, identification and tracking.
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Generation of Authentic Latent Fingerprints Background
Gajda, Adam ; Goldmann, Tomáš (referee) ; Kanich, Ondřej (advisor)
This bachelor's thesis deals with the generation of authentic latent fingerprint backgrounds, through the use of deep learning, more specifically with the help of conditional generative adversarial network and other more conventional methods. This work summarizes the basic theoretical information about biometrics including synthetic fingerprints and a introduction into artificial intelligence. The main model proposed in this thesis has not come into fruition due to lack of unique training data. Other possible reasons were discussed. Thus an alternative way of generating latent fingerprint backgrounds was developed and after visual evaluation of the final results and real data the conclusion was positive.
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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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Detection and Recognition of Gun in a Scene
Stuchlík, David ; Goldmann, Tomáš (referee) ; Drahanský, Martin (advisor)
The aim of the diploma thesis is to design an algorithm for detection and recognition of the type of gun in the image. Firstly, the existing methods and techniques for detecting the various objects are briefly introduced in the text of the thesis however, the methods are primarily focused on guns. Next, the basics of neural networks are briefly outlined, followed by an overview of the most common detectors for deep neural networks. The second half of the thesis is devoted to the implementation of an application for generating images based on a 3D model of a gun, the creation of a data file and learning of a neural network. Finally, the results obtained, which clearly indicate that in order to cover a huge variation of real weapons, is necessary to generate a large amount of training data based on many different 3D models, are briefly summarized in the conclusion of the thesis.
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