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Computer Vision Group Prof. Daniel CremersComputer Vision Group Prof. Daniel Cremers
Machine Learning for Applications in Computer Vision
Neural Networks andDeep Learning
Philip Häusser
Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
Recap: What is Learning?• problem (“classification”)
• data (images + labels)• model (linear regression)• cost function• optimization algorithm
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Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
Recap: What is Learning?• problem (“classification”)
• data (images + labels)• model (linear regression)• cost function• optimization algorithm
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What is “Deep”?
Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
Recap: What is Learning?• problem (“classification”)
• data (images + labels)• model (linear regression)• cost function• optimization algorithm
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What is “Deep”?
Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
Perceptron• The human brain (1010 cells) is the archetype of neural networks
http://home.agh.edu.pl/~vlsi/AI/intro/
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Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
Neuron Activations• Different type of activation functions
Threshold activation (binary classifier)
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Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
Neuron Activations• Different type of activation functions
Sigmoid activation
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Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
Neuron Activations• Different type of activation functions
rectified Linear Unit activation
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Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
Simple Neural Network
• good reference: neuralnetworksanddeeplearning.com
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Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
Simple Neural Network
Why is it good?
• inspired by brain• highly non-linear → function approximator• shown to solve complicated tasks
successfully
• “self-organizing map” / parameters are learned
• data driven
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Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
Simple Neural Network
Why is it bad?
• inspired by brain• highly non-linear• high degree of freedom• shown to solve complicated tasks
successfully
• “self-organizing map”→ parameters are learned
• data driven• not much theory yet
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Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
Back Propagation• Define a cost function
• Goal: Find global minimum
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predictionground truth
Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
Back Propagation
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Gradient Descent
predictionground truth
• Define a cost function
• Goal: Find global minimum
Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
Back Propagation
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Learning rate
predictionground truth
• Define a cost function
• Minimize the error (derivative of the cost) with gradient descentGradient descent update rule:
Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
Back Propagation• Define a cost function
• Propagate the error through layers•Take the derivative of the output of the layer w.r.t the input of the
layer•Apply update rule for the parameters
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predictionground truth
Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
MNIST Digit Classification with NN
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Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
Convolutional Neural Networks
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Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
Convolutional Neural Networks
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Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
Convolutional Neural Networks
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Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
Convolutional Neural Networks
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Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
Convolutional Neural Networks
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AlexNetKrizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton."Imagenet classification with deep convolutional neural networks.”Advances in neural information processing systems. 2012.
Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
Convolutional Neural Networks
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Convolutional networks take advantage of the properties of natural signals:
• local connections • shared weights
• pooling • the use of many layers
Person
FlowNet: Learning Optical Flowwith Convolutional Networks
Philipp Fischer, Alexey Dosovitskiy, Eddy Ilg, Thomas BroxPhilip Häusser, Caner Hazırbaş, Vladimir Golkov, Daniel Cremers, Patrick van der Smagt
Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
Flying Chairs
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Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
Data Augmentation
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Gen
erat
edA
ugm
ente
d
• translation, rotation, scaling, additive Gaussian noise• changes in brightness, contrast, gamma and colour
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Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
FlowNetSimple
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Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
FlowNetSimple - Flying Chairs
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Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
FlowNetSimple - Sintel
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Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
FlowNetSimple + Variational Smoothing
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Philip HaeusserComputer Vision Group
Machine Learning for Computer Vision
FlowNet: Learning Optical Flowwith Convolutional Networks
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