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Author: Lukman
Achmad Lukman
Thesis Title: Deep Learning Approach For Object Recognition and Knowledge Transfer
Deep Learning Approach For Object Recognition and Knowledge Transfer
Advisor: 楊傳凱
Chuan-Kai Yang
Committee: Yuan-Cheng Lai
Yuan-Cheng Lai
Bor-Shen Lin
Bor-Shen Lin
孫敏德
Min-Te (Peter) Sun
Yen-Hung Chen
Yen-Hung Chen
Degree: 博士
Doctor
Department: 管理學院 - 資訊管理系
Department of Information Management
Thesis Publication Year: 2023
Graduation Academic Year: 111
Language: 英文
Pages: 65
Keywords (in other languages): Angular view, Full deep distillation mutual learning, Half deep distillation mutual learning
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  • Computer vision and machine learning are two methods that can combine to solve object recognition tasks. When the computer vision method extracts the features from image datasets,Machine learning uses the featuresto learn a model and then deploys the model on a testing or unseen example.A Convolutional neural network is one of the popular methods to address image classification task problems. This method is a part of developing Machine learning that combines computer vision and machine learning that, makes us easy to use without thinking about how to extract the features with another method.
    In this thesis, we proposed methods that can help to increase the performance of convolutional neural networks in two scenarios: object recognition and knowledge transfer learning. The first part of this thesis addresses how to collect specific 3D data for object recognition. We offer an angular view algorithm, and the integrated system uses incremental learning differently. In the second part, we study how to enhance the performance of deep mutual learning and combine it with the distillation knowledge method in the knowledge transfer problem. The goal is to produce two new approaches. We first made an angular view algorithm and rotating display tools to create a new dataset and use it to recognize images from inside and outside the lab. Secondly, We develop the integrated system with GoogleNetwithan angular view algorithm combined with two existing pre-trained models: AlexNet, and VGG16 weights. This system can recognize the new task without forgetting the old one inside the weight-pre-trained model using incremental training. Another way to increase the performance of Deep learning using the knowledge transfer paradigm, we formulatedthe new approach methods Full Deep Distillation Mutual Learning (FDDML) and Half Deep Distillation Mutual Learning (HDDML). Weconducted extensive experiments on three open-source datasets, including CIFAR-100, TinyImageNet, and Cinic-10.

    ABSTRACT i ACKNOWLEDGMENTS iii CONTENTS iv LIST OF FIGURES vii LIST OF TABLES ix 1. INTRODUCTION 1 1.1 Motivation 1 1.2 Outline and contributions 2 1.2.1 Part I Object recognition using angular view technique 2 1.2.2 Part II Transfer Knowledge with FDDML and HDDML 3 2. OBJECT RECOGNITION USING ANGULAR VIEW TECHNIQUE 4 2.1 Overview 4 2.2 Literature review 6 2.3 Research method 8 2.3.1 Angular view algorithm 9 2.3.2 Segmentation 11 2.3.3 The performance of Angular view effect on the System 12 2.3.4 Integrated system 13 2.3.5 Comparison with the others incremental learning methods 15 2.4 Experiments 16 2.4.1 Datasets and evaluation 16 2.4.2 Experimental procedure 18 2.4.3 Evaluation 19 2.4.4 Comparison with others 24 2.4.5 Evaluation of the integrated system 25 2.5 Conclusion 28 3. KNOWLEDGETRANSFER WITH FDDML AND HDDML 29 3.1 Overview 29 3.2 Literature review 32 3.3 The proposed FDDML and HDDML model 33 3.3.1 DML and KD 34 3.3.2 Full Deep Distillation Mutual Learning(FDDML) 36 3.3.3 Half Deep Distillation Mutual Learning(HDDML) 38 3.4 Experiments 39 3.4.1 Dataset 39 3.4.2 Neural Net Models 39 3.4.2.1. On CIFAR-100 Training and Testing 39 3.4.2.2. On TinyImageNet 64×64 Image Size Training and Testing 40 3.4.2.3. On TinyImageNet 32×32 Downsampled Image Size Training and Testing 40 3.4.2.4. On Cinic-10 32 × 32 with image Size Training and Testing 40 3.4.3 Implementation Details 40 3.4.4 Evaluation on CIFAR-100 41 3.4.5 Evaluation on TinyImageNet 43 3.4.6 Evaluation on Cinic-10 46 3.5 Conclusion 48 4. LIMITATION AND FUTURE DIRECTIONS 50 REFERENCES 51

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