A foundation for exact binarized morphological neural networks
Theodore Aouad Hugues Talbot
Training and running deep neural networks (NNs) often demands a lot of
computation and energy-intensive specialized hardware (e.g. GPU, TPU...). One
way to reduce the computation and power cost is to use binary weight NNs, but
these are hard to train because the sign function has a non-smooth gradient. We
present a model based on Mathematical Morphology (MM), which can binarize
ConvNets without losing performance under certain conditions, but these
conditions may not be easy to satisfy in real-world scenarios. To solve this,
we propose two new approximation methods and develop a robust theoretical
framework for ConvNets binarization using MM. We propose as well regularization
losses to improve the optimization. We empirically show that our model can
learn a complex morphological network, and explore its performance on a
classification task.