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You're reading from  Deep Learning with Theano

Product typeBook
Published inJul 2017
PublisherPackt
ISBN-139781786465825
Edition1st Edition
Tools
Theano
Concepts
Deep Learning
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Author (1)
Christopher Bourez
Christopher Bourez
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Christopher Bourez

Christopher Bourez graduated from Ecole Polytechnique and Ecole Normale Suprieure de Cachan in Paris in 2005 with a Master of Science in Math, Machine Learning and Computer Vision (MVA). For 7 years, he led a company in computer vision that launched Pixee, a visual recognition application for iPhone in 2007, with the major movie theater brand, the city of Paris and the major ticket broker: with a snap of a picture, the user could get information about events, products, and access to purchase. While working on missions in computer vision with Caffe, TensorFlow or Torch, he helped other developers succeed by writing on a blog on computer science. One of his blog posts, a tutorial on the Caffe deep learning technology, has become the most successful tutorial on the web after the official Caffe website. On the initiative of Packt Publishing, the same recipes that made the success of his Caffe tutorial have been ported to write this book on Theano technology. In the meantime, a wide range of problems for Deep Learning are studied to gain more practice with Theano and its application.
Read more about Christopher Bourez

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Summary

The spatial transformer layer is an original module to localize an area of the image, crop it and resize it to help the classifier focus on the relevant part in the image, and increase its accuracy. The layer is composed of differentiable affine transformation, for which the parameters are computed through another model, the localization network, and can be learned via backpropagation as usual.

An example of the application to reading multiple digits in an image can be inferred with the use of recurrent neural units. To simplify our work, the Lasagne library was introduced.

Spatial transformers are one solution among many others for localizations; region-based localizations, such as YOLO, SSD, or Faster RCNN, provide state-of-the-art results for bounding box prediction.

In the next chapter, we'll continue with image recognition to discover how to classify full size images that contain a lot more information than digits, such as natural images of indoor scenes and outdoor landscapes...

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Deep Learning with Theano
Published in: Jul 2017Publisher: PacktISBN-13: 9781786465825
© 2017 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Christopher Bourez

Christopher Bourez graduated from Ecole Polytechnique and Ecole Normale Suprieure de Cachan in Paris in 2005 with a Master of Science in Math, Machine Learning and Computer Vision (MVA). For 7 years, he led a company in computer vision that launched Pixee, a visual recognition application for iPhone in 2007, with the major movie theater brand, the city of Paris and the major ticket broker: with a snap of a picture, the user could get information about events, products, and access to purchase. While working on missions in computer vision with Caffe, TensorFlow or Torch, he helped other developers succeed by writing on a blog on computer science. One of his blog posts, a tutorial on the Caffe deep learning technology, has become the most successful tutorial on the web after the official Caffe website. On the initiative of Packt Publishing, the same recipes that made the success of his Caffe tutorial have been ported to write this book on Theano technology. In the meantime, a wide range of problems for Deep Learning are studied to gain more practice with Theano and its application.
Read more about Christopher Bourez

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