Spatial Transformer Networks (STN) is a differentiable module that can be inserted between convolution layers to provide Equivariance to the image or features.
Convolution Neural Networks apply a convolution filter, on the input image in the first layer, and then on the feature maps. The CNN’s have provided extra-ordinary results by using the same weights of the filter over several parts of the same image. The results were further boosted by adding a Max Pooling layer between the Convolution layers. This pooling layer not only reduced the parameters but also improved the performance of the CNN models.
This combination of CNN and the Pooling layer provides translational in-variance: can predict a particular object only if the object is moved around in the image. However, the orientation and shape of the object should not change much. This becomes a major drawback of CNN when used in a real-world setting.
The first idea that comes to my mind is to find some black-box that could transform the image to an ideal standard so that the same classification could be re-used. Deep Mind did the same thing, they proposed a module called Spatial Transformer Network, which performs transformation to an input image. This model can be regarded as an attention module to any spatial input. Let’s have a look at the nuts and bolts of this algorithm.
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