Author: Lu, Chongkai
Title: A study on spatial transformer networks
Advisors: Chi, Zheru George (EIE)
Degree: M.Sc.
Year: 2019
Subject: Hong Kong Polytechnic University -- Dissertations
Image processing
Neural networks (Computer science)
Machine learning
Department: Faculty of Engineering
Pages: xi, 64 pages : color illustrations
Language: English
Abstract: Neural networks have already shown their excellent performance on image recognition, while most of them perform well only when test samples are aligned well. However, in real world, most of the targets required detection and recognition in images are various in size, rotation and location. Spatial Transformer Networks (STN) as an end-to-end neural network which combine learning alignment and classification into one neural network, can learn to align targets automatically in cooperation with a classification neural network. STN shows good performance on image recognition on spatially variant datasets. We implemented STN and found the averaging problem of STN. We propose a new training strategy for STN called STP-NN (Spatial Transformer Pretrained-Neural Networks). Experimental results on MNIST shows that STP-NN can achieve a better performance than normal STN, decreasing the error rate on spatially the distorted MNIST dataset from 2.75% to 1.86%. Moreover, shown in our experimental results, STP-NN can align the rotated images well but STN shows a bad performance on these images.
Rights: All rights reserved
Access: restricted access

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