1 Restricted Boltzmann Machines and Applications Pattern Recognition (IC6304) [Presentation Date: ] [ Ph.D Candidate, MLV Lab Muhammad Aasim Rafique

Restricted Boltzmann Machine

3 본 과제의 기본 정보 Restricted Boltzmann Machine (RBM) Generative Model and Unsupervised Learning Dimensionality reduction, Classification, Collaborative filtering, feature l earning etc Visible and Hidden Layer neurons has binary states only Energy Function Distribution Free Energy

4 본 과제의 기본 정보 Restricted Boltzmann Machine (RBM) Conditional Probabilities Probabilities of Binary states Free Energy

5 본 과제의 기본 정보 Contrastive Divergence Methodology Contd.

6 본 과제의 기본 정보 Contrastive Divergence Contd. Methodology Contd.

Examples

8 본 과제의 기본 정보 Simple RBM Visible units = Hidden = 5, 7, 11 Learning rates Epochs

9 본 과제의 기본 정보 Mnist Handwritten digits Hand written digits 60000, 28 x 28 images, training images 10000, test images Train RBM with 28x28 visible units visualRBM.exe

10 본 과제의 기본 정보 Background Subtraction Problem A given scene(Background) Change in the scene(foreground object) Discover the changes in two sce nes (Background subtraction)

11 본 과제의 기본 정보 Background Subtraction Methodology(learning Model) Images are converted into binary RBM with binary visible and binary hidden layer unit is initialized No of visible units = no of pixels No of hidden units = vary with video set (8 in this case) v 120 x 160

12 본 과제의 기본 정보 Background Subtraction Methodology Contd. Learning RBM will learn the most probable scene i.e. background in le arning phase An image will be presented to the visible nodes RBM runs through the positive and negative phases of CD RBM will sample the changed scene to the learned backgrou nd The weights(receptive fields) are adjusted to learn the scene Learning Parameters Learning rate : No of training epochs: 5 Training examples: frames are enough

13 본 과제의 기본 정보 RBM Background Learning vvv

14 본 과제의 기본 정보 Background Subtraction Methodology Contd. Testing (Background Subtraction) Test frame is converted to binary Binary data extracted from frame is clamped to visible neuro ns The hidden layers neurons probabilities are computed Visible layers is reconstructed and gives the background Background is subtracted pixel by pixel from the original test frame

15 본 과제의 기본 정보 RBM Background Constructio n vvv

16 본 과제의 기본 정보 Experiment Original Colored Image Binary ImageSample Test Images

17 본 과제의 기본 정보 Receptive Fields

18 본 과제의 기본 정보 Results

19 본 과제의 기본 정보 Challenges with Binary RBM Images and videos are not always binary or grayscale Converting colored images to binary image loses important infor mation Binary RBM does not represent the colored images well The pixel base comparison with thresh-hold is not possible using 0-1 representation of a pixel value

Gaussian Bernoulli RBM

21 본 과제의 기본 정보 Gaussian Bernoulli RBM Visible layer neurons are Gaussian with real valued input Hidden layer neurons are Bernoulli/Binary Energy Function Conditional Probabilities Variance Learning

22 본 과제의 기본 정보 GRBM BS Methodology Each frame of color video is sliced in RGB channel

Deep Learning: Deep Belief Nets

24 본 과제의 기본 정보 Deep learning

25 본 과제의 기본 정보 Deep Belief Nets

26 본 과제의 기본 정보 Convolution Nets

27 본 과제의 기본 정보 Recurrent Nets

28 본 과제의 기본 정보 References yann.lecun.com/ cs.stanford.edu/people/ang/