2018
https://er.ucu.edu.ua:443/handle/1/1188
2024-03-28T13:52:38ZGeneration of code from text description with syntactic parsing and Tree2Tree model
https://er.ucu.edu.ua:443/handle/1/1191
Generation of code from text description with syntactic parsing and Tree2Tree model
Stehnii, Anatolii
Software development requires vast knowledge of different programming tools which cannot be kept in human memory. Therefore software developers often formulate their task in human language to query online knowledge bases like StackOverflow to get short snippets of code. In this work, we explored the way of code generation from natural language description and prepared web API for Python which translates NL descriptions to short snippets of code. Our model implements sequence-to-sequence model with recursive encoder and uses syntactic trees instead of plain sequence on input. Results have not outperformed current state-of-the-art performance. However, presented Tree2Tree model has potential in other applications and this work makes a solid base for a further research.
2018-01-01T00:00:00ZApplication of Generative Neural Models for Style Transfer Learning in Fashion
https://er.ucu.edu.ua:443/handle/1/1190
Application of Generative Neural Models for Style Transfer Learning in Fashion
Mykhailych, Mykola
The purpose of this thesis is to analyze different generative adversarial networks for application in fashion. Research of “mode collapse” problem of generative adversarial networks. We studied the theoretical part of the “mode collapse” and conducted experiments on a synthetic toy dataset, and a dataset containing real data from fashion. Due to the developed method, it was possible to achieve visible results of improving the quality of garment generation by solving the problem of collapse.
2018-01-01T00:00:00ZConditional Adversarial Networks for Blind Image Deblurring
https://er.ucu.edu.ua:443/handle/1/1189
Conditional Adversarial Networks for Blind Image Deblurring
Kupyn, Orest
We present an end-to-end learning approach for motion deblurring,
which is based on conditional GAN and content loss – DeblurGAN.
DeblurGAN achieves state-of-the art in structural similarity measure
and by visual appearance. The quality of the deblurring model is also
evaluated in a novel way on a real-world problem – object detection
on (de-)blurred images. The method is 5 times faster than the closest
competitor.
Second, we present a novel method of generating synthetic motion
blurred images from the sharp ones, which allows realistic dataset
augmentation.
2018-01-01T00:00:00Z