Special applications: Face recognition & Neural style transfer
Discover how CNNs can be applied to multiple fields, including art generation and face recognition. Implement your own algorithm to generate art and recognize faces!
(the idea of a blank net or deep blank is a very popular way of naming algorithms in the deep learning world)
Face Verification and Binary Classification
So, this is a way to treat face recognition just as a binary classification problem. And this is an alternative to the triplet loss for training a system like this.
유사도를 측정하는 여러가지 variation이 존재
예) Chi-square similarity
Neural Style Transfer
What is neural style transfer?
What are deep ConvNets learning?
These are the nine patches that cause one hidden unit to be highly activated. And then each grouping, this is a different set of nine image patches that cause one hidden unit to be activated.
train hidden units in layer 1, they're often looking for relatively simple features such as edge or a particular shade of color.
later units are actually seen larger image patches
correlation 으로 표현하는 부분은 수학적으로 엄밀히 unnormalized cross covariance 임. 직관적인 표현을 위해 correlation으로 표현
평균을 빼는 항이 없기 때문
각 layer의 Style Matrix 간 차이의 Frobenius norm을 구하고 모두 합한다.
1D and 3D Generalizations
2D 데이터 뿐만 아니라 1D/3D 데이터도 CNN을 적용할 수 있음
데이터 차원에 대한 Generalizations
Practice questions
Quiz: Special applications: Face recognition & Neural style transfer
Programming assignments
Programming Assignment: Art generation with Neural Style Transfer
content cost
style cost
Generated image correlated with content image
Now, we initialize the "generated" image as a noisy image created from the content_image.
The generated image is slightly correlated with the content image.
By initializing the pixels of the generated image to be mostly noise but slightly correlated with the content image, this will help the content of the "generated" image more rapidly match the content of the "content" image.
Special applications: Face recognition & Neural style transfer
Face Recognition
What is face recognition?
Face Verification
Face Recognition
One Shot Learning
Siamese Network
Triplet Loss
Face Verification and Binary Classification
Neural Style Transfer
What is neural style transfer?
What are deep ConvNets learning?
Cost Function
Content Cost Function
Style Cost Function
동일 layer의 필터간 상관관계
![image](https://user-images.githubusercontent.com/21363169/91311345-f2809580-e7ed-11ea-82af-087374da72a5.png)
Gram Matrix (Style Matrix)
correlation 으로 표현하는 부분은 수학적으로 엄밀히 unnormalized cross covariance 임. 직관적인 표현을 위해 correlation으로 표현
각 layer의 Style Matrix 간 차이의 Frobenius norm을 구하고 모두 합한다.
![Screenshot from 2020-08-27 11-38-44](https://user-images.githubusercontent.com/21363169/91377279-019d2d00-e85a-11ea-830d-b1109ba29021.png)
1D and 3D Generalizations
Practice questions
Quiz: Special applications: Face recognition & Neural style transfer
Programming assignments
Programming Assignment: Art generation with Neural Style Transfer
content cost
style cost
Generated image correlated with content image
Now, we initialize the "generated" image as a noisy image created from the content_image.
Programming Assignment: Face Recognition
기타
Kaggle 실습
tensorflow turotials / generative / neural style transfer