  We revisit the idea of using deep neural networks for one-shot decoding of
random and structured codes, such as polar codes. Although it is possible to
achieve maximum a posteriori (MAP) bit error rate (BER) performance for both
code families and for short codeword lengths, we observe that (i) structured
codes are easier to learn and (ii) the neural network is able to generalize to
codewords that it has never seen during training for structured, but not for
random codes. These results provide some evidence that neural networks can
learn a form of decoding algorithm, rather than only a simple classifier. We
introduce the metric normalized validation error (NVE) in order to further
investigate the potential and limitations of deep learning-based decoding with
respect to performance and complexity.
