Recurrent Spatial Transformer Networks

Recurrent Spatial Transformer Networks

Søren Kaae Sønderby,Casper Kaae Sønderby,Lars Maaløe,Ole Winther

(Submitted on 17 Sep 2015)

We integrate the recently proposed spatial transformer network (SPN) [Jaderberg et. al 2015] into a recurrent neural network (RNN) to form an RNN-SPN model. We use the RNN-SPN to classify digits in cluttered MNIST sequences. The proposed model achieves a single digit error of 1.5% compared to 2.9% for a convolutional networks and 2.0% for convolutional networks with SPN layers. The SPN outputs a zoomed, rotated and skewed version of the input image. We investigate different down-sampling factors (ratio of pixel in input and output) for the SPN and show that the RNN-SPN model is able to down-sample the input images without deteriorating performance. The down-sampling in RNN-SPN can be thought of as adaptive down-sampling that minimizes the information loss in the regions of interest. We attribute the superior performance of the RNN-SPN to the fact that it can attend to a sequence of regions of interest.

Subjects:Computer Vision and Pattern Recognition (cs.CV)

Cite as:arXiv:1509.05329[cs.CV]

(orarXiv:1509.05329v1[cs.CV]for this version)

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