Applications of Deep Learningin Computer Vision

Christoph Körner

Outline

1) Introduction to Neural Networks

2) Deep Learning

3) Applications in Computer Vision

4) Conclusion

Why Deep Learning?

● Wins every computer vision challenge (classification, segmentation, etc.)

● Can be applied in various domains (speech recognition, game prediction, computer vision, etc.)

● Beats human accuracy● Big communities and resources● Hardware for Deep Learning

Perceptron (1958)

● Weighted sum of inputs● Threshold operator

Artificial Neural Network (1960)

● Universal function approximator● Can solve the XOR problem

Backpropagation (1982)

● Propagate the error through the network● Allows Optimization (SGD, etc.)● Enables training of multi-layer networks

Convolution and Pooling (1989)

● Less parameters than hidden layers● More efficient training

Handwritten ZIP Codes (1989)

● 30 training passes● Achieved 92% accuracy

What happened until 2011?

● Better Initialization● Better Non-linearities: ReLU● 1000 times more training data● More computing power

● Factor 1 million speedup in training time through parallelization on GPUs

Deep Learning

● Conv-, Pool- and Fully-Connected Layers● ReLU activations● Deep nested models with many parameters● New layer types and structures● New techniques to reduce overfitting● Loads of training data and compute power

● 10.000.000 images● Weeks of training on multi-GPU machines

AlexNet (2012)

● 62.378.344 parameters (250MB)● 24 layers

VGGNet (2013)

● 102.908.520 parameters (412MB)● 23 layers

GoogLeNet (2014)

● 6.998.552 parameters (28MB)● 143 layers

Inception Module

● Heavy use of 1x1 convolutions (applied along the depth dimension)

● Very efficient

ResNet (2015)

● Residual learning● 152 layers

Applications in Computer Vision

Classification

● One class per image● Softmax layer at the end

Localization

● Bounding box Regression● Sigmoid layer with 4 outputs at the end

● Via Classification

Detection

● Multiple Objects, multiple classes● Solved using multiple networks

Segmentation

More Applications

● Compression● Auto-encoders, Self-organizing maps

● Image Captioning● Solved with Recurrent Architecture

● Image Stylization● Clustering● Many more...

Conclusion

● Powerful, learn from data instead of hand-crafted feature extraction● Better than humans

● Deeper is always better● Overfitting

● More data is always better● Data quality● Ground truth

Thank you!

Christoph Körner