論文の要旨
題 目 A Study on Machine Learning for Image Processing using GPUs(GPU を用いた画像処理に対する機械学習の研究)
氏名松村 直樹

Machine learning is an application of artiﬁcial intelligence that makes systems learn the ability andimprove from experience automatically without being explicitly programmed. In recent years, machinelearning has been developing rapidly with the developing of Convolutional Neural Networks (CNNs). To
reduce the computation time of them, Graphics Processing Units (GPU) is mainly used for accelerator. In
this dissertation, we research in machine learning for image processing using the GPUs.
In Chapter 1, we describe the introduction of the dissertation including the research background andcontributions. We then go on to introduce the CNNs and its training ﬂow in Chapter 2. The CNNs arecomposed of convolutional layers and Fully-Connected Layers (FCLs), and applicable to various applications such as image classiﬁcation and scene classiﬁcation. Also, the CNNs are used for subroutines inimage transformation networks such as Generative Adversarial Networks and pix2pix.
In Chapter 3, we describe NVIDIA GPU architecture and CUDA. Latest GPUs are designed for general
purpose computing that include the computation of machine learning. They can perform computation inapplications traditionally handled by the CPU.
In Chapter 4, we present two tile art image generation algorithms using greedy algorithm and machinelearning as a greedy approach and a machine learning approach, respectively. The goodness of a generated
tile art image is deﬁned by the error between its projected image onto human eyes and the original image.
The projected image is simulated on the computer using a two-dimensional Gaussian ﬁlter. From thegoodness, the approach generates tile art images by pasting tiles one by one to the white canvas image.
However, it takes a large amount of computation time, therefore, we implement the greedy approach on
the GPU. The greedy approach on NVIDIA TITAN V GPU can run up to 318 times faster than thaton CPU with single thread. The machine learning approach aims to generate tile art images by image

transformation networks. As the training dataset, we have used the tile art images generated by the greedy
approach. Also, we have adopted the pix2pix as the tile art image generation network. As a trainingresult, the network can generate a tile art image of size 4096 × 3072 within 1.04 seconds while the greedy
approach on the GPU takes 571 seconds. However, in the tile art images generated by the machine learning
approach, some tiles have lack of edge and noises that are not included in the greedy approach. Therefore,
we propose an improvement technique of the machine learning approach to generate a high quality tile
art image using iterative inference. As a result, in the tile art image with iterative inference technique, thecharacteristics of tiles can be enhanced.
In Chapter 5, we present a novel structured sparse FCL in the CNNs for image classiﬁcation problem.
The proposed approach eliminates the connections between the input and the output nodes except for thosein the same position of the feature maps. Since the proposed architecture is deﬁned initially, it is suitable toparallel computation. Therefore, we introduce an eﬃcient implementation for the proposed sparse FCLs
on the GPU. As a result for the large scale image recognition dataset, the proposed approach achieves
a 14.7 times compression with 0.68% top-1 accuracy and 0.19% top-5 accuracy decrease for AlexNet
network, and a 21.3 times compression with 0.68% top-1 accuracy and 0.31% top-5 accuracy decrease
for VGG-16 network. Also, in the experiment on NVIDIA RTX 2080 Ti GPU, the GPU implementation
for the proposed FCLs achieves speed-up factor 14.97 and 16.67 for forward and backward propagationcompared to that for the non-compressed FCLs, respectively. Furthermore, to conﬁrm that our proposed
approach is applicable to practical image classiﬁcation problems, we have trained the proposed models
using transfer learning on various datasets. The experimental results show the proposed approach canachieve high test accuracy with high compression ratio on each dataset.
In Chapter 6, we conclude our works.