9. How does Neural Network learn?
Calculate error
Sum of squares loss
Softmax loss
Cross entropy loss
Hinge loss
10. How does Neural Network learn?
−
Sum of squares loss
Softmax loss
Cross entropy loss
Hinge loss
0.2
0.8
Sum of squares loss = 0.08
0.2
0.8
Output of ANN
0.0
1.0
Target value
= 0.04
0.04
( )
2
12. What we have to decide?
Gradient Descent Optimization Algorithms
• Batch Gradient Descent
• Stochastic Gradient Descent (SGD)
• Momentum
• Nesterov Accelerated Gradient (NAG)
• Adagrad
• RMSProp
• AdaDelta
• Adam
13. What we have to decide?
Neural network structure
• VGG-19
• GoogLeNet
Training techniques
• Drop out
• sparse
Loss function and cost function
• Cross entropy
• Sum of squeares
Optimization algorithm
• Adam
• SDG
14. Why it’s hard to decide a loss function?
In classification.
Input
NN
Output of NN Target
Output of NN
Calculate NN output Calculate loss
loss
NN
Update weights
of NN using loss
15. Why it’s hard to decide a loss function?
In classification.
Output of NN Target
0.67
0.00
0.02
0.12
0.04
0.00
0.03
0.14
1.0
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Loss
Sum of L1 norm Cross entropy
0.68 2.45
16. Why it’s hard to decide a loss function?
When an output of NN is image.
Input Ground truth L1
This image is captured from Phillip Isola, et al., “Image-to-Image with Conditional Adversarial Networks”,
CVPR, 2016
17. Why it’s hard to decide a loss function?
If output form is a digit.
Multiple choice questions
Essay questions
Art practical exam
If output form is a image.
18. Why it’s hard to decide a loss function?
If output form is a digit.
Multiple choice questions
Essay questions
Art practical exam
If output form is a image.
A difficulty of assessment
33. Generative Adversarial Nets
D tries to make D(G(z)) near 0, G tries to make D(G(z)) near 1
This image is captured from Ian J. Goodfellow, et al., “Generative Adversarial Nets”.
37. Introduce
Conditional adversarial nets are a general-purpose solution
for image-to-image translation.
Code: https://github.com/phillipi/pix2pix
This image is captured from Phillip Isola, et al., “Image-to-Image with Conditional Adversarial Networks”,
CVPR, 2016
38. Method
GAN
G: z y
Conditional GAN
G: {x, z} y
This image is captured from Phillip Isola, et al., “Image-to-Image with Conditional Adversarial Networks”,
CVPR, 2016
39. Method
ℒ 𝑐𝐺𝐴𝑁(𝐺, 𝐷) = 𝔼 𝑥,𝑦 log 𝐷 𝑥, 𝑦 + 𝔼 𝑥,𝑧[log(1 − 𝐷 𝑥, 𝐺(𝑥, 𝑧) )]
ℒ 𝐺𝐴𝑁(𝐺, 𝐷) = 𝔼 𝑦 log 𝐷 𝑦 + 𝔼 𝑥,𝑧[log(1 − 𝐷 𝐺(𝑥, 𝑧) )]
ℒ 𝐿1(𝐺) = 𝔼 𝑥,𝑦,𝑧 𝑦 − 𝐺(𝑥, 𝑧) 1
𝐺∗ = 𝑎𝑟𝑔 min
𝐺
max
𝐷
ℒ 𝑐𝐺𝐴𝑁 𝐺, 𝐷 + 𝜆 ℒ 𝐿1(𝐺)
Objective function for GAN
Objective function for cGAN
Final objective function
40. Method
Network architectures
Generator
Discriminator – Markovian discriminator (PatchGAN)
This discriminator effectively models the image as a Markov random field.
This image is captured from Phillip Isola, et al., “Image-to-Image with Conditional Adversarial Netowrks”,
CVPR, 2016
41. Method
This image is captured from Phillip Isola, et al., “Image-to-Image with Conditional Adversarial Nets”,
https://www.slideshare.net/xavigiro/imagetoimage-translation-with-conditional-adversarial-nets-upc-reading-group
This image is captured from http://ccvl.jhu.edu/datasets/
42. Experiments
This image is captured from Phillip Isola, et al., “Image-to-Image with Conditional Adversarial Networks”,
CVPR, 2016
43. Experiments
This image is captured from Phillip Isola, et al., “Image-to-Image with Conditional Adversarial Networks”,
CVPR, 2016
44. Experiments
This image is captured from Phillip Isola, et al., “Image-to-Image with Conditional Adversarial Networks”,
CVPR, 2016