Visualizing Neural Networks

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Visualizing Neural Networks

Habibi Aghdam, Hamed / Jahani Heravi, Elnaz

Abstract Understanding behavior of neural networks is necessary in order to better analyze and diagnose them. Quantitative metrics such as classification accuracy and F1 score just give us numbers indicating how good is the classifier in our problem. They do not tell us how a neural network achieves this result. Visualization is a set of techniques that are commonly used for understanding structure of high-dimensional vectors. In this chapter, we briefly reviewed data-driven techniques for visualization and showed that how to apply them on neural networks. Then, we focused on techniques that visualize neural networks by minimizing an objective function. Among them, we explained three different methods. In the first method, we defined a loss function and found an image that maximizes the classification score of a particular class. In order to generate more interpretable images, the objective function was regularized using $L_{2}$ norm of the image. In the second method, gradient of a particular neuron was computed with respect to the input image and it is illustrated by computing its magnitude. The third method formulated the visualizing problem as an image reconstruction problem. To be more specific, we explained a method that tries to find an image in which the representation of this image is very close to the representation of the original image. This technique usually tells us what information is usually discarded by a particular layer.