Computational Analysis of Learned Representations in Deep Neural Network Classifiers

摘要

When a neural network is trained for a specific task, activations of the hidden units encode internal representations of the inputs. Models formulated in a layer-wise fashion are believed to structure such representations in a hierarchical fashion, increasing in complexity and abstractness towards the output layer, in an analogy to both biological neural networks and artificially constructed computational models. This paper examines how the structure of classification tasks manifests itself in these internal representations, using a variety of ad hoc metrics. The results, based on feedforward neural networks trained on moderately complex datasets MNIST and SVHN, confirm our hypothesis that the hidden neurons become more correlated with class information towards the output layer, providing some evidence for an increasing bottom-up organization in representations. While various activation functions lead to noticeably different internal representations as measured by each of the methods, the differences in overall classification accuracy remain minute. This confirms the intuition that there exist qualitatively different solutions to the complex classification problem imposed by nonlinearities in the hidden layers.