  We present a new approach to learn compressible representations in deep
architectures with an end-to-end training strategy. Our method is based on a
soft (continuous) relaxation of quantization and entropy, which we anneal to
their discrete counterparts throughout training. We showcase this method for
two challenging applications: Image compression and neural network compression.
While these tasks have typically been approached with different methods, our
soft-to-hard quantization approach gives results competitive with the
state-of-the-art for both.
