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Inspired by the adaptation phenomenon of neuronal firing, we propose theregularity normalization (RN) as an unsupervised attention mechanism (UAM)which computes the statistical regularity in the implicit space of neuralnetworks under the Minimum Description Length (MDL) principle. Treating theneural network optimization process as a partially observable model selectionproblem, the regularity normalization constrains the implicit space by anormalization factor, the universal code length. We compute this universal codeincrementally across neural network layers and demonstrate the flexibility toinclude data priors such as top-down attention and other oracle information.Empirically, our approach outperforms existing normalization methods intackling limited, imbalanced and non-stationary input distribution in imageclassification, classic control, procedurally-generated reinforcement learning,generative modeling, handwriting generation and question answering tasks withvarious neural network architectures. Lastly, the unsupervised attentionmechanisms is a useful probing tool for neural networks by tracking thedependency and critical learning stages across layers and recurrent time stepsof deep networks.

Regularity Normalization: Neuroscience-Inspired Unsupervised Attention across Neural Network Layers