用NAS自动设计生成尼泊尔数字的手写图像，效果远超传统方法。
NepScript Genesis: Neural Architecture Search for Handwritten Devanagari Digit Synthesis
- 通过两阶段搜索+领域感知评分，自动优化生成对抗网络结构。
- 生成质量提升76.19%（FID降至79.12），且在1小时内完成训练。
- 适合低资源场景下的手写数字数据增强，可提升分类准确率5.5个百分点。
本文提出NepScript Genesis，一种用于条件性天城文手写数字合成的神经架构搜索（NAS）框架。对比五种NAS策略与精心设计的深度卷积生成对抗网络（DCGAN）基线（FID=332.28），结果表明自适应探索策略取得最佳质量-效率平衡：FID降至79.12（改进76.19%），模式覆盖率最高（召回率=0.531），且训练时间不足1个GPU小时。此外，将书写体特异性结构先验引入搜索阶段，有效防止早期模式崩溃。下游低资源实验显示，以最优NAS模型生成的数字扩充每类仅250个真实样本，使CNN分类准确率从91.0%提升至96.5%（+5.5个百分点），证明NAS优化的合成数据足以提升实际识别系统的性能。
原文摘要 · Abstract (English)
This paper introduces NepScript Genesis, a Neural Architecture Search (NAS) framework for automated Generative Adversarial Network (GAN) discovery, applied to conditional Devanagari handwritten digit synthesis. We compare five NAS strategies against a carefully constructed Deep Convolutional GAN (DCGAN) baseline (FID=332.28). Architecture selection utilizes a two-stage pipeline guided by a novel domain-aware evaluation metric (Enhanced Score). Results demonstrate that Adaptive Exploration achieves the optimal quality-efficiency trade-off, attaining an FID of 79.12 -- a 76.19% improvement over the baseline -- and the highest mode coverage among the NAS strategies (Recall=0.531) in under one GPU-hour. Furthermore, we demonstrate that incorporating script-specific structural heuristics into the search phase prevents early-stage mode collapse. In a downstream low-resource evaluation, augmenting 250 real training samples per class with GAN-generated digits from the best NAS model improves CNN classification accuracy from 91.0% to 96.5% (+5.5 percentage points), demonstrating that NAS-optimized synthesis produces digits of sufficient quality to benefit practical recognition pipelines when real data is scarce.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。