用Transformer学习中度敏感的二维码解码，突破纠错极限。
Learning Moderately Input-Sensitive Functions: A Case Study in QR Code Decoding
- 用Transformer学习二维码结构化数据，实现端到端解码
- 在训练数据外成功解码随机字符串，超越理论纠错极限
- 专注数据位而忽略纠错位，机制不同于传统解码器
学习函数的难度与其输入敏感性相关。图像分类任务输入不敏感，而算术与符号计算高度敏感。本文首次提出基于学习的二维码（QR code）解码方法，研究中度敏感函数。实验表明，Transformer可成功解码二维码，甚至突破理论纠错极限，且能从英文数据泛化至其他语言及随机字符串。此外，模型主要关注数据位，忽略纠错位，揭示出与传统解码器不同的工作机制。
原文摘要 · Abstract (English)
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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