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머신러닝 시그 세미나_(deep learning for visual recognition)

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http://vision0814.tistory.com/

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머신러닝 시그 세미나_(deep learning for visual recognition)

  1. 1. Observable Data Model Information
  2. 2. Observable Data Model Information Speech Recognition I am a boy
  3. 3. Observable Data Model Information Image Classify Cat
  4. 4. Observable Data Model Information HOW?
  5. 5. • Core visual object recognition Feedback
  6. 6. • Weakness in kernel machine(SVM …): • It does not scale well with sample size. • Based on matching local templates. • the training data is referenced for test data • Local representation VS distributed representation • N N(Neural Network) -> Kernel machine -> Deep NN
  7. 7. • Deep learning is all about deep neural networks • 1949 : Hebbian learning • Donald Hebb : the father of neural networks • 1958 : (single layer) Perceptron • Frank Rosenblatt - Marvin Minsky, 1969 • 1986 : Multilayer Perceptron(Back propagation) • David Rumelhart, Geoffrey Hinton, and Ronald Williams • 2006 : Deep Neural Networks • Geoffrey Hinton and Ruslan Salakhutdinov
  8. 8. Hand- Crafted Features Trainable Generic Classifier
  9. 9. F(X;𝜃) 𝜃 Simple Classifier
  10. 10. Layer 1 Simple Classifier Layer 2 Layer N
  11. 11. Layer 1 Simple Classifier Layer 2 Layer N
  12. 12. Layer 1 Simple Classifier Layer 2 Layer N
  13. 13. Layer 1 Simple Classifier Layer 2 Layer N
  14. 14. Layer 1 Simple Classifier Layer 2 Layer N Trainable Generic Classifier Hand- crafted Features
  15. 15. Layer 1 Simple Classifier Layer 2 Layer N Trainable Generic Classifier Hand- crafted Features
  16. 16. Layer 1 Simple Classifier Layer 2 Layer N
  17. 17. Shallow learning Deep learning feature extraction by domain experts (SIFT, SURF, orb...) automatic feature extraction from data separate modules (feature extractor + trainable classifier) unified model : end-to-end learning (trainable feature + trainable classifier)
  18. 18. i j
  19. 19. • http://www.cs.toronto.edu/~hinton/MatlabForSciencePaper.html • convolutional neural networks (popular): LeCun • Alex Krizhevsky: Hinton (python, C++) • https://code.google.com/p/cuda-convnet/ • Caffe: UC Berkeley (C++) • http://caffe.berkeleyvision.org/

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