Normalization methods are a central building block in the deep learning toolbox. They accelerate and stabilize training, while decreasing the dependence on manually tuned learning rate schedules. When learning from multi-modal distributions, the effectiveness of batch normalization (BN), arguably the most prominent normalization method, is reduced. As a remedy, we propose a more flexible approach: by extending the normalization to more than a single mean and variance, we detect modes of data on-the-fly, jointly normalizing samples that share common features. We demonstrate that our method outperforms BN and other widely used normalization techniques in several experiments, including single and multi-task datasets.
We present a novel normalization method for deep neural networks that is robust to multi-modalities in intermediate feature distributions. Normalization method that learns multi-modal distribution in the feature space Proposes a generalization of Batch Normalization under the assumption that the statistics of the unit activations over the batches and over the spatial dimensions is not unimodal