이미지 분류를 위한 앙상블 방법

학위논문(석사)--서울대학교 대학원 :자연과학대학 통계학과,2019. 8. 박병욱.In this paper, Deep Convolutional Neural Network (CNN) with various structures and loss functions are verified. Our data is VGGFace2 which is widely spread datasets. Using CNN models like VGG and ResNet, with cross entropy, cosface loss, and arcface loss, verify performance each models at first. Later, we use stacking method for ensemble. Also, unlike any other Face image classification problem, we used face detection to improve performance. So, First, do face detection so that we can focus only on each persons identity. Second, using face image, construct convolution layers. And then the last, gather all convolution neural network results and summarize them. Focusing on model structures and tunning procedures, we verify that the performance of model with ensemble get better than without ensemble.본 논문에서는 다양한 구조와 손실 함수를 갖는 합성곱 신경망을 검증한다. VGGFace2 데이터를 이용하여 교차 엔트로피, Cosine 손실 및 Arcface 손실과 함께 VGG 및 ResNet과 같은 합성곱 신경망 모델을 사용하여 각 모델의 성능을 검증한다. 또한 앙상블을 위해 스태킹을 사용한다. 알고리즘은 먼저 얼굴 인식을 수행하여 각 사람의 신원에만 집중할 수있게 하여 기존 얼굴 이미지 분류 문제와 달리 얼굴 검색을 사용하여 성능을 향상 시켰다. 그리고 얼굴 이미지를 사용하여 합성곱 신경망을 구성 한다. 마지막으로 모든 신경망 결과를 수집하고 앙상블하여 모델을 검증한다. 본 논문은 모델 구조와 무작위 검색을 통한 튜닝 절차에 중점을 두었으며, 앙상블을 통하여 기존 모델의 성능이 향상됨을 확인하였다.1 Introduction 1 2 Model Structures 4 2.1 Baseline models . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 Structured models . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.3 Loss functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.4 Stacking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3 Data 10 3.1 Data collecting . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.2 Data preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . 11 4 Simulation and Evaluation 13 4.1 Training methods . . . . . . . . . . . . . . . . . . . . . . . . . . 13 4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 5 Discussion 18 References 21 국문초록 24 List of Figures 3.1 Extracted face on VGGFace2 dataset on the same identity . . . 12 4.1 Training loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 5.1 Non-normalized plot . . . . . . . . . . . . . . . . . . . . . . . . 19 5.2 Normalized plot on training . . . . . . . . . . . . . . . . . . . . 20 List of Tables 4.1 Tuning hyperparameters . . . . . . . . . . . . . . . . . . . . . . 16 4.2 Tuning result . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 4.3 Stacking result . . . . . . . . . . . . . . . . . . . . . . . . . . . 17Maste

소재 조성 도출용 라이브러리 생성 장치 및 방법

The present invention provides an apparatus and a method for constructing a library for deriving a material composition using empirical result. Which enables acceleration of research on the material-properties relationship. By applying the empirical results of the material composition, missing data of the material compositions can be statistically calculated by using supervised non-linear imputation techniques. The completed composition information of the materials is passed as an input of machine learning material-properties relationship prediction