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Facenet (2015)
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Math
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ccss17
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Math
Math
Foundation of Math
Foundation of Math
History of Math
Formal System
Incompleteness theorem
Turing's proof
Consistency proof of Peano arithmetic
Set Theory
Set Theory
Set
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ZFC axiom system
Arithmetic Operations
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Hyperreal Numbers
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Paper
Paper
Performance of Python runtimes on a non-numeric scientific code
The NumPy array: a structure for efficient numerical computation
Facenet (2015)
Facenet (2015)
FaceNet 사전지식
얼굴 인식
FaceNet 모델
FaceNet 구현 오픈소스
얼굴 인식을 위한 얼굴 탐색
MTCNN 구현 오픈소스
FaceNet 논문을 위한 사전 수학지식
norm 거리, 벡터 사이의 거리
폐구간
《FaceNet: A Unified Embedding for Face Recognition and Clustering》
Abstract
1. Introduction
FaceNet 과 지금까지의 얼굴 인식 접근법과의 차이
triplets 선정
논문 구조 설명
2. Related Work
3. Method
3.1. Triplet Loss
3.2. Triplet Selection
3.3. Deep Convolutional Networks
4. Datasets and Evaluation
4.1. Hold-out Test Set
4.2. Personal Photos
4.3. Academic Datasets
5. Experiments
5.1. Computation Accuracy Trade-off
5.2. Effect of CNN Model
5.3. Sensitivity to Image Quality
5.4. Embedding Dimensionality
5.5. Amount of Training Data
5.6. Performance on LFW
5.7. Performance on Youtube Faces DB
5.8. Face Clustering
6. Summary
Quadruplet (2017)
BlueBorne
Computer
Computer
Nand to Tetris
Why the future doesn't need us
Rust Memo
ProgrammerBase
ProgrammerBase
README
Contents
Contents
Day 1
Day 2
Day 3
Day 4
Day 5
Docker
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Information
Git
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Vim
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security tutorial
security tutorial
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Day1 Base
Day2 Computer Principle 1
Day3 Computer Principle 2
Day4 Reversing 1
Day5 Reversing 2
Day6 Exploit 1
Day7 Exploit 2
Day8 Exploit 3
Day9 Exploit 4
Day10 Pentesting
FaceNet 사전지식
얼굴 인식
FaceNet 모델
FaceNet 구현 오픈소스
얼굴 인식을 위한 얼굴 탐색
MTCNN 구현 오픈소스
FaceNet 논문을 위한 사전 수학지식
norm 거리, 벡터 사이의 거리
폐구간
《FaceNet: A Unified Embedding for Face Recognition and Clustering》
Abstract
1. Introduction
FaceNet 과 지금까지의 얼굴 인식 접근법과의 차이
triplets 선정
논문 구조 설명
2. Related Work
3. Method
3.1. Triplet Loss
3.2. Triplet Selection
3.3. Deep Convolutional Networks
4. Datasets and Evaluation
4.1. Hold-out Test Set
4.2. Personal Photos
4.3. Academic Datasets
5. Experiments
5.1. Computation Accuracy Trade-off
5.2. Effect of CNN Model
5.3. Sensitivity to Image Quality
5.4. Embedding Dimensionality
5.5. Amount of Training Data
5.6. Performance on LFW
5.7. Performance on Youtube Faces DB
5.8. Face Clustering
6. Summary
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