Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding

The paper demonstrates that incorporating code automorphisms into syndrome-based neural decoding (SBND) improves the models’ learning and generalization through data augmentation during training and inference. By applying this technique to short, high-rate codes, the authors achieve performance close to maximum likelihood decoding (MLD) using small datasets and appropriate training. The study also indicates that previous SBND results may have underestimated their true error‑correction capability due to insufficient training.

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