This week you'll learn about Spatial Transformer Networks Using TensorFlow.

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Convolutional Neural Networks (CNNs) have redefined the impact of machine learning in today's world. Keeping the spatial information of images alive as it is passed through the weight matrices in a neural network boosts results. It also paves the way for analysis from a human perspective since we, too, assess images with their spatial construction intact. 

However, CNNs, despite their undeniable prowess, are not void of flaws. A major problem that CNNs face is transformational variance. Since the result of the network is dependent on the data, a simple rotation of an input image can mess up the network's predictions. 

Our task in this tutorial is to implement a module that helps the network decide what kind of transformation is required for an input image so that the image becomes discernible by the network, which leads to a correct prediction. This module is known as the "Spatial Transformer Network."

The big picture: We need to devise ways to make our networks more robust. Creating a dataset with many augmentations is unfeasible from an efficiency viewpoint, so we come to the Spatial Transformer Network. This module enables CNNs to decide what transformation is necessary to fix the image to a point where the prediction is correct. 

How it works: The Spatial Transformer Network introduces three separate parts that work in unison. We get what transformation might be needed and create an output grid, after which interpolation methods are used to deduce the coordinates' output pixel values. 

Our thoughts: Since it tackles the issue of transformational variance, it is a brilliant approach to make CNNs even more robust for tackling real-world applications. 

Yes, but: Model interpretability remains an issue, as transformations will be based on what the model thinks is right, and they might not make much sense to the human eyes. 

Stay smart: Tinker with the parameters and assess the changes yourself to better understand the impact of this module as a whole. 

Click here to read the full tutorial

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