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Face recognition face verification one shot learning
- 1. Copyright Notice These slides are distributed under the Creative Commons License. DeepLearning.AI makes these slides available for educational purposes. You may not use or distribute these slides for commercial purposes. You may make copies of these slides and use or distribute them for educational purposes as long as you cite DeepLearning.AI as the source of the slides. For the rest of the details of the license, see https://creativecommons.org/licenses/by-sa/2.0/legalcode
- 2. deeplearning.ai Face recognition What is face recognition?
- 3. Andrew Ng Face recognition [Courtesy of Baidu]
- 4. Andrew Ng Face verification vs. face recognition Verification • Input image, name/ID • Output whether the input image is that of the claimed person Recognition • Has a database of K persons • Get an input image • Output ID if the image is any of the K persons (or “not recognized”)
- 6. Andrew Ng One-shot learning Learning from one example to recognize the person again
- 7. Andrew Ng Learning a “similarity” function d(img1,img2) = degree of difference between images If d(img1,img2) ≤ - > -
- 9. Andrew Ng Siamese network [Taigman et. al., 2014. DeepFace closing the gap to human level performance] ⋮ ⋮ "($) ⋮ ⋮ "(&)
- 10. Andrew Ng Goal of learning ⋮ f("($) ) ⋮ Parameters of NN define an encoding ((" ) ) Learn parameters so that: If " ) , " + are the same person, f " ) − f " + & is small. If " ) , " + are different persons, f " ) − f " + & is large.
- 12. Andrew Ng Learning Objective [Schroff et al.,2015, FaceNet: A unified embedding for face recognition and clustering] Anchor Positive Anchor Negative
- 13. Andrew Ng Loss function Training set: 10k pictures of 1k persons [Schroff et al.,2015, FaceNet: A unified embedding for face recognition and clustering]
- 14. Andrew Ng Choosing the triplets A,P,N During training, if A,P,N are chosen randomly, ! ", $ + & ≤ !(", )) is easily satisfied. Choose triplets that’re “hard” to train on. [Schroff et al.,2015, FaceNet: A unified embedding for face recognition and clustering]
- 15. Andrew Ng Training set using triplet loss Anchor Positive Negative ⋮ ⋮ ⋮
- 16. deeplearning.ai Face recognition Face verification and binary classification
- 17. Andrew Ng Learning the similarity function ⋮ f($(%)) ⋮ f($(')) $(%) $(') ( ) [Taigman et. al., 2014. DeepFace closing the gap to human level performance]
- 18. Andrew Ng Face verification supervised learning $ ( 1 0 0 1 [Taigman et. al., 2014. DeepFace closing the gap to human level performance]
- 19. deeplearning.ai What is neural style transfer? Neural Style Transfer
- 20. Andrew Ng Neural style transfer [Images generated by Justin Johnson] Content Style Style Content Generated image Generated image
- 21. deeplearning.ai What are deep ConvNets learning? Neural Style Transfer
- 22. Andrew Ng Visualizing what a deep network is learning 224×224×3 ⋮ ⋮ & ' 110×110×96 55×55×96 26×26×256 13×13×256 13×13×384 13×13×384 6×6×256 FC 4096 FC 4096 Pick a unit in layer 1. Find the nine image patches that maximize the unit’s activation. Repeat for other units. [Zeiler and Fergus., 2013, Visualizing and understanding convolutional networks]
- 23. Andrew Ng Visualizing deep layers Layer 2 Layer 4 Layer 3 Layer 5 Layer 1
- 24. Andrew Ng Visualizing deep layers: Layer 1 Layer 2 Layer 4 Layer 3 Layer 5 Layer 1
- 25. Andrew Ng Visualizing deep layers: Layer 2 Layer 2 Layer 4 Layer 3 Layer 5 Layer 1
- 26. Andrew Ng Visualizing deep layers: Layer 3 Layer 2 Layer 4 Layer 3 Layer 5 Layer 1
- 27. Andrew Ng Visualizing deep layers: Layer 3 Layer 2 Layer 4 Layer 3 Layer 5 Layer 1
- 28. Andrew Ng Visualizing deep layers: Layer 4 Layer 2 Layer 4 Layer 3 Layer 5 Layer 1
- 29. Andrew Ng Visualizing deep layers: Layer 5 Layer 2 Layer 4 Layer 3 Layer 5 Layer 1
- 31. Andrew Ng Neural style transfer cost function Content C Style S Generated image G [Gatys et al., 2015. A neural algorithm of artistic style. Images on slide generated by Justin Johnson]
- 32. Andrew Ng Find the generated image G 1. Initiate G randomly G: 100×100×3 2. Use gradient descent to minimize %(') [Gatys et al., 2015. A neural algorithm of artistic style]
- 34. Andrew Ng Content cost function • Say you use hidden layer ! to compute content cost. " # = % "'()*+)* ,, # + / "0*12+ (4, #) • Use pre-trained ConvNet. (E.g., VGG network) • Let 6[2](9) and 6[2](:) be the activation of layer ! on the images • If 6[2](9) and 6[2](:) are similar, both images have similar content [Gatys et al., 2015. A neural algorithm of artistic style]
- 35. deeplearning.ai Style cost function Neural Style Transfer
- 36. Andrew Ng Meaning of the “style” of an image ⋮ " # 255 134 93 22 123 94 83 2 34 44 187 30 34 76 232 124 67 83 194 142 255 134 202 22 123 94 83 4 34 44 187 192 34 76 232 34 67 83 194 94 255 231 42 22 123 94 83 2 34 44 187 92 34 76 232 124 67 83 194 202 Say you are using layer $’s activation to measure “style.” Define style as correlation between activations across channels. How correlated are the activations across different channels? %& %' %( [Gatys et al., 2015. A neural algorithm of artistic style]
- 37. Andrew Ng Intuition about style of an image Style image Generated Image %& %' %( %& %' %( [Gatys et al., 2015. A neural algorithm of artistic style]
- 38. Andrew Ng Style matrix Let a*,,,- [/] = activation at 2, 3, 4 . 7[/] is n9 [/] ×n9 [/] [Gatys et al., 2015. A neural algorithm of artistic style]
- 39. Andrew Ng Style cost function ;<=>/? / (A, 7) = 1 2%' [/] %& [/] %( [/] E F F(7--G / H − 7--G / J ) -G - [Gatys et al., 2015. A neural algorithm of artistic style]
- 40. deeplearning.ai Convolutional Networks in 1D or 3D 1D and 3D generalizations of models
- 41. Andrew Ng Convolutions in 2D and 1D 14×14 2D input image ∗ 2D filter 5×5 ∗ 1 3 10 3 1 1 20 15 3 18 12 4 17
- 53. Andrew Ng 3D convolution ∗ 3D volume 3D filter