Exploring the Properties and Evolution of Neural Network Eigenspaces during Training
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In this work we explore the information processing inside neural networks using logistic regression probes <cit.> and the saturation metric <cit.>. We show that problem difficulty and neural network capacity affect the predictive performance in an antagonistic manner, opening the possibility of detecting over- and under-parameterization of neural networks for a given task. We further show that the observed effects are independent from previously reported pathological patterns like the “tail pattern” described in <cit.>. Finally we are able to show that saturation patterns converge early during training, allowing for a quicker cycle time during analysis
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