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arXiv · 2506.20305

Learning Moderately Input-Sensitive Functions: A Case Study in QR Code Decoding

Kazuki Yoda·Kazuhiko Kawamoto·Hiroshi Kera
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Abstract

The hardness of learning a function that attains a target task relates to its input-sensitivity. For example, image classification tasks are input-insensitive as minor corruptions should not affect the classification results, whereas arithmetic and symbolic computation, which have been recently attracting interest, are highly input-sensitive as each input variable connects to the computation results. This study presents the first learning-based Quick Response (QR) code decoding and investigates learning functions of medium sensitivity. Our experiments reveal that Transformers can successfully decode QR codes, even beyond the theoretical error-correction limit, by learning the structure of embedded texts. They generalize from English-rich training data to other languages and even random strings. Moreover, we observe that the Transformer-based QR decoder focuses on data bits while ignoring error-correction bits, suggesting a decoding mechanism distinct from standard QR code readers.

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Kazuki Yoda, Kazuhiko Kawamoto, Hiroshi Kera. 2025-06-25. Learning Moderately Input-Sensitive Functions: A Case Study in QR Code Decoding. https://arxiv.org/abs/2506.20305

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