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