Learning Morphological Transformations with Recurrent Neural Networks

Deep learning techniques have been successfully used in recent years to learn useful image trans- formations and features, thus contributing significantly to the advancements in neural networks. However deep nets suffer from the drawback that they require large training times and mul- tifarious parameters that need to be hand tuned for optimal performance. In this paper we investigate the use Recurrent neural network architectures to learn useful transformations of an image(object), progressively over time. Learning these latent transformations enables the recur- rent architecture to correctly predict, to a high degree of accuracy, the original representation of an object from its transformed representations.