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San Kim

The document discusses neural networks, generative adversarial networks, and image-to-image translation. It begins by explaining how neural networks learn through forward propagation, calculating loss, and using the loss to update weights via backpropagation. Generative adversarial networks are introduced as a game between a generator and discriminator, where the generator tries to fool the discriminator and vice versa. Image-to-image translation uses conditional GANs to translate images from one domain to another, such as maps to aerial photos.

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Ⅰ. Neural Network Ⅱ. Generative Adversarial Nets Ⅲ. Image-to-Image Translation
1. How does Neural Network learn? 2. What do we have to decide? 3. Why it’s hard to decide a loss function? Neural Network Ⅰ
What is the Neural Network?
How does Neural Network learn? Preparing input and target pairs. inputs targets Lion Cat map 0 1 1 0 0 1 One-hot encoding Dog 2 0 0 0 0 1
How does Neural Network learn? The weights of the network are arbitrarily set. 0.6 0.2 0.3 0.9 0.1
How does Neural Network learn? Feed Forward
How does Neural Network learn? Feed Forward 0.2 0.1 0.6 0.3 0.2 0.7 0.3 0.1 𝑠𝑢𝑚: 0.2 × 0.2 + 0.1 × 0.7 + 0.6 × 0.3 + 0.3 × 0.1 = 0.32 N21 𝑂𝑢𝑡𝑝𝑢𝑡 𝑜𝑓 𝑁21 = 𝑓 0.32 𝑓 𝑖𝑠 𝑎𝑐𝑡𝑖𝑣𝑎𝑡𝑖𝑜𝑛 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛 𝑜𝑓 𝑁21 𝑂𝑢𝑡𝑝𝑢𝑡 𝑜𝑓 𝑁21 = 𝑓 0.32 = 0.1024. 𝑖𝑓 𝑓 𝑥 = 𝑥2
How does Neural Network learn? Calculate error Sum of squares loss Softmax loss Cross entropy loss Hinge loss
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
How does Neural Network learn? Feedback
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
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
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
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
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
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.
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
1. Generative Adversarial Networks 2. Training Tip Generative Adversarial Nets Ⅱ
Generative Adversarial Nets Leonardo Dicaprio: a counterfeiter Tom Hanks: FBI – a counterfeit money discriminator
Generative Adversarial Nets Counterfeiter (Generator) FBI (Discriminator) 50,000 won Can you discriminate it is counterfeit or not? I made a counterfeit money!
Generative Adversarial Nets Counterfeiter (Generator) FBI (Discriminator) Oh, no! I can’t discriminate it is counterfeit or not. Maybe it’s counterfeit money with 55% probability! 50,000 won
Generative Adversarial Nets FBI (Discriminator) 50,000 won Compare target and output of generator. Output of generator target
Generative Adversarial Nets Counterfeiter (Generator) FBI (Discriminator) 50,000 won Can you discriminate it is counterfeit or not? I made a counterfeit money!
Generative Adversarial Nets Counterfeiter (Generator) FBI (Discriminator) 50,000 won It’s counterfeit money with 99.9% probability! loss
Generative Adversarial Nets FBI (Discriminator) 50,000 won Compare target and output of generator. Output of generator target
Generative Adversarial Nets Counterfeiter (Generator) FBI (Discriminator) Can you discriminate it is counterfeit or not? I made a counterfeit money again!
Generative Adversarial Nets Counterfeiter (Generator) FBI (Discriminator) It’s counterfeit money with 70.5% probability! loss
Generative Adversarial Nets FBI (Discriminator) Compare target and output of generator. Output of generator target
Generative Adversarial Nets Counterfeiter (Generator) FBI (Discriminator) Can you discriminate it is counterfeit or not? I made a counterfeit money again!
Generative Adversarial Nets Counterfeiter (Generator) FBI (Discriminator) Oh, no! I can’t discriminate it is counterfeit or not. loss Maybe it’s counterfeit money with 50% probability!
Generative Adversarial Nets FBI (Discriminator) Compare target and output of generator. Output of generator target
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”.
Training Tip min 𝐺 max 𝐷 𝑉(𝐷, 𝐺) = 𝔼 𝑥~𝑝 𝑑𝑎𝑡𝑎(𝑥) log 𝐷 𝑥 + 𝔼 𝑧~𝑝 𝑧(𝑧)[log(1 − 𝐷 𝐺(𝑧) )] max 𝐺 𝑉(𝐷, 𝐺) = 𝔼 𝑧~𝑝 𝑧(𝑧)[log(𝐷 𝐺(𝑧) )] max 𝐷 𝑉(𝐷, 𝐺) = 𝔼 𝑥~𝑝 𝑑𝑎𝑡𝑎(𝑥) log 𝐷 𝑥 + 𝔼 𝑧~𝑝 𝑧(𝑧)[log(1 − 𝐷 𝐺(𝑧) )] min 𝐺 𝑉(𝐷, 𝐺) = −(𝔼 𝑧~𝑝 𝑧(𝑧)[log(𝐷 𝐺(𝑧) )]) min 𝐷 𝑉(𝐷, 𝐺) = −(𝔼 𝑥~𝑝 𝑑𝑎𝑡𝑎(𝑥) log 𝐷 𝑥 + 𝔼 𝑧~𝑝 𝑧(𝑧)[log(1 − 𝐷 𝐺(𝑧) )])
Training Tip min 𝐺 max 𝐷 𝑉(𝐷, 𝐺) = 𝔼 𝑥~𝑝 𝑑𝑎𝑡𝑎(𝑥) log 𝐷 𝑥 + 𝔼 𝑧~𝑝 𝑧(𝑧)[log(1 − 𝐷 𝐺(𝑧) )] max 𝐺 𝑉(𝐷, 𝐺) = 𝔼 𝑧~𝑝 𝑧(𝑧)[log(𝐷 𝐺(𝑧) )] max 𝐷 𝑉(𝐷, 𝐺) = 𝔼 𝑥~𝑝 𝑑𝑎𝑡𝑎(𝑥) log 𝐷 𝑥 + 𝔼 𝑧~𝑝 𝑧(𝑧)[log(1 − 𝐷 𝐺(𝑧) )] min 𝐺 𝑉(𝐷, 𝐺) = −(𝔼 𝑧~𝑝 𝑧(𝑧)[log(𝐷 𝐺(𝑧) )]) min 𝐷 𝑉(𝐷, 𝐺) = −(𝔼 𝑥~𝑝 𝑑𝑎𝑡𝑎(𝑥) log 𝐷 𝑥 + 𝔼 𝑧~𝑝 𝑧(𝑧)[log(1 − 𝐷 𝐺(𝑧) )])
1. Introduce 2. Method 3. Experiments Image to Image translation Ⅲ
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
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
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
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
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/
Experiments This image is captured from Phillip Isola, et al., “Image-to-Image with Conditional Adversarial Networks”, CVPR, 2016
Experiments This image is captured from Phillip Isola, et al., “Image-to-Image with Conditional Adversarial Networks”, CVPR, 2016
Experiments This image is captured from Phillip Isola, et al., “Image-to-Image with Conditional Adversarial Networks”, CVPR, 2016
Experiments Patch size variations. This images are captured from Phillip Isola, et al., “Image-to-Image with Conditional Adversarial Networks”, CVPR, 2016
References [1] Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio, “Generative Adversarial Nets”, NIPS 2014 [2] Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros, “Image-to-Image Translation with Conditional Adversarial Networks”, CVPR 2016 [3] Kwangil Kim, “Artificial Neural Networks”, Multimedia system lecture of KHU, 2017 [4] DL4J, “A Beginner’s Guide to Recurrent Networks and LSTMs”, 2017, https://deeplearning4j.org/lstm.html. Accessed, 2018-01-29 [5] Phillip Isola, Jun-Yan Zhu, Tinghui, “Image-to-Image translation with conditional Adversarial Nets”, Nov 25, 2016, https://www.slideshare.net/xavigiro/imagetoimage-translation-with-conditional- adversarial-nets-upc-reading-group. Accessed, 2018-01-29 [6] CCVL, “Datasets: PASCAL Part Segmentation Challenge”, 2018 http://ccvl.jhu.edu/datasets/. Accessed, 2018-01-29
Gan seminar

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Gan seminar

  • 1.
  • 2. Ⅰ. Neural Network Ⅱ. Generative Adversarial Nets Ⅲ. Image-to-Image Translation
  • 3. 1. How does Neural Network learn? 2. What do we have to decide? 3. Why it’s hard to decide a loss function? Neural Network Ⅰ
  • 4. What is the Neural Network?
  • 5. How does Neural Network learn? Preparing input and target pairs. inputs targets Lion Cat map 0 1 1 0 0 1 One-hot encoding Dog 2 0 0 0 0 1
  • 6. How does Neural Network learn? The weights of the network are arbitrarily set. 0.6 0.2 0.3 0.9 0.1
  • 7. How does Neural Network learn? Feed Forward
  • 8. How does Neural Network learn? Feed Forward 0.2 0.1 0.6 0.3 0.2 0.7 0.3 0.1 𝑠𝑢𝑚: 0.2 × 0.2 + 0.1 × 0.7 + 0.6 × 0.3 + 0.3 × 0.1 = 0.32 N21 𝑂𝑢𝑡𝑝𝑢𝑡 𝑜𝑓 𝑁21 = 𝑓 0.32 𝑓 𝑖𝑠 𝑎𝑐𝑡𝑖𝑣𝑎𝑡𝑖𝑜𝑛 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛 𝑜𝑓 𝑁21 𝑂𝑢𝑡𝑝𝑢𝑡 𝑜𝑓 𝑁21 = 𝑓 0.32 = 0.1024. 𝑖𝑓 𝑓 𝑥 = 𝑥2
  • 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
  • 11. How does Neural Network learn? Feedback
  • 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
  • 19. 1. Generative Adversarial Networks 2. Training Tip Generative Adversarial Nets Ⅱ
  • 20. Generative Adversarial Nets Leonardo Dicaprio: a counterfeiter Tom Hanks: FBI – a counterfeit money discriminator
  • 21. Generative Adversarial Nets Counterfeiter (Generator) FBI (Discriminator) 50,000 won Can you discriminate it is counterfeit or not? I made a counterfeit money!
  • 22. Generative Adversarial Nets Counterfeiter (Generator) FBI (Discriminator) Oh, no! I can’t discriminate it is counterfeit or not. Maybe it’s counterfeit money with 55% probability! 50,000 won
  • 23. Generative Adversarial Nets FBI (Discriminator) 50,000 won Compare target and output of generator. Output of generator target
  • 24. Generative Adversarial Nets Counterfeiter (Generator) FBI (Discriminator) 50,000 won Can you discriminate it is counterfeit or not? I made a counterfeit money!
  • 25. Generative Adversarial Nets Counterfeiter (Generator) FBI (Discriminator) 50,000 won It’s counterfeit money with 99.9% probability! loss
  • 26. Generative Adversarial Nets FBI (Discriminator) 50,000 won Compare target and output of generator. Output of generator target
  • 27. Generative Adversarial Nets Counterfeiter (Generator) FBI (Discriminator) Can you discriminate it is counterfeit or not? I made a counterfeit money again!
  • 29. Generative Adversarial Nets FBI (Discriminator) Compare target and output of generator. Output of generator target
  • 30. Generative Adversarial Nets Counterfeiter (Generator) FBI (Discriminator) Can you discriminate it is counterfeit or not? I made a counterfeit money again!
  • 31. Generative Adversarial Nets Counterfeiter (Generator) FBI (Discriminator) Oh, no! I can’t discriminate it is counterfeit or not. loss Maybe it’s counterfeit money with 50% probability!
  • 32. Generative Adversarial Nets FBI (Discriminator) Compare target and output of generator. Output of generator target
  • 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”.
  • 34. Training Tip min 𝐺 max 𝐷 𝑉(𝐷, 𝐺) = 𝔼 𝑥~𝑝 𝑑𝑎𝑡𝑎(𝑥) log 𝐷 𝑥 + 𝔼 𝑧~𝑝 𝑧(𝑧)[log(1 − 𝐷 𝐺(𝑧) )] max 𝐺 𝑉(𝐷, 𝐺) = 𝔼 𝑧~𝑝 𝑧(𝑧)[log(𝐷 𝐺(𝑧) )] max 𝐷 𝑉(𝐷, 𝐺) = 𝔼 𝑥~𝑝 𝑑𝑎𝑡𝑎(𝑥) log 𝐷 𝑥 + 𝔼 𝑧~𝑝 𝑧(𝑧)[log(1 − 𝐷 𝐺(𝑧) )] min 𝐺 𝑉(𝐷, 𝐺) = −(𝔼 𝑧~𝑝 𝑧(𝑧)[log(𝐷 𝐺(𝑧) )]) min 𝐷 𝑉(𝐷, 𝐺) = −(𝔼 𝑥~𝑝 𝑑𝑎𝑡𝑎(𝑥) log 𝐷 𝑥 + 𝔼 𝑧~𝑝 𝑧(𝑧)[log(1 − 𝐷 𝐺(𝑧) )])
  • 35. Training Tip min 𝐺 max 𝐷 𝑉(𝐷, 𝐺) = 𝔼 𝑥~𝑝 𝑑𝑎𝑡𝑎(𝑥) log 𝐷 𝑥 + 𝔼 𝑧~𝑝 𝑧(𝑧)[log(1 − 𝐷 𝐺(𝑧) )] max 𝐺 𝑉(𝐷, 𝐺) = 𝔼 𝑧~𝑝 𝑧(𝑧)[log(𝐷 𝐺(𝑧) )] max 𝐷 𝑉(𝐷, 𝐺) = 𝔼 𝑥~𝑝 𝑑𝑎𝑡𝑎(𝑥) log 𝐷 𝑥 + 𝔼 𝑧~𝑝 𝑧(𝑧)[log(1 − 𝐷 𝐺(𝑧) )] min 𝐺 𝑉(𝐷, 𝐺) = −(𝔼 𝑧~𝑝 𝑧(𝑧)[log(𝐷 𝐺(𝑧) )]) min 𝐷 𝑉(𝐷, 𝐺) = −(𝔼 𝑥~𝑝 𝑑𝑎𝑡𝑎(𝑥) log 𝐷 𝑥 + 𝔼 𝑧~𝑝 𝑧(𝑧)[log(1 − 𝐷 𝐺(𝑧) )])
  • 36. 1. Introduce 2. Method 3. Experiments Image to Image translation Ⅲ
  • 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
  • 45. Experiments Patch size variations. This images are captured from Phillip Isola, et al., “Image-to-Image with Conditional Adversarial Networks”, CVPR, 2016
  • 46. References [1] Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio, “Generative Adversarial Nets”, NIPS 2014 [2] Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros, “Image-to-Image Translation with Conditional Adversarial Networks”, CVPR 2016 [3] Kwangil Kim, “Artificial Neural Networks”, Multimedia system lecture of KHU, 2017 [4] DL4J, “A Beginner’s Guide to Recurrent Networks and LSTMs”, 2017, https://deeplearning4j.org/lstm.html. Accessed, 2018-01-29 [5] Phillip Isola, Jun-Yan Zhu, Tinghui, “Image-to-Image translation with conditional Adversarial Nets”, Nov 25, 2016, https://www.slideshare.net/xavigiro/imagetoimage-translation-with-conditional- adversarial-nets-upc-reading-group. Accessed, 2018-01-29 [6] CCVL, “Datasets: PASCAL Part Segmentation Challenge”, 2018 http://ccvl.jhu.edu/datasets/. Accessed, 2018-01-29