應用於工業有限資料的結構化嵌入研究

Abstract

在品質檢測與異常偵測等工業應用中，資料不足與標註成本高昂常限制深度學習模型的效能。深度生成模型，特別是生成對抗網路（Generative Adversarial Networks, GANs），已在影像合成與資料增強方面展現出卓越能力。然而，傳統GAN需大量且多樣化的訓練資料，才能達到穩定且可靠的表現，這在工業領域中往往難以實現。因此，在少量資料條件下訓練的GAN經常出現不穩定性與模式崩潰（mode collapse）問題。 本研究提出一種新穎的方法，透過將傳統隨機雜訊輸入替換為由真實資料經降維技術（包括主成分分析（PCA）與局部線性嵌入（LLE））所提取的結構化嵌入（Structured Embeddings, SEs），以改善在此情境下的資料生成。本方法不修改GAN架構，而是提升潛在空間的資訊量，為模型學習提供具意義的歸納偏差（inductive bias）。 本研究將SE整合至InfoGAN架構中，藉由其可解釋潛變數的學習能力支援可控變異，同時採用包含真實與生成資料的迭代式訓練策略以增強學習成效。實驗結果顯示，所提出方法在生成穩定性、樣本品質及多樣性方面均優於傳統GAN，且在對抗性攻擊情境下展現更佳的魯棒性。 本研究結果證實，結構化潛在空間能有效促進穩定且語意一致的生成，為資料不足之工業環境中的資料驅動學習提供一種具實用性且完整的方法。

In industrial applications such as quality inspection and anomaly detection, data scarcity and high labeling costs often limit the effectiveness of deep learning models. Deep generative models, particularly Generative Adversarial Networks (GANs), have demonstrated remarkable capabilities in image synthesis and data augmentation. However, conventional GANs require large and diverse datasets to achieve stable and reliable performance, which is challenging to obtain in industrial domains. As a result, GANs trained under limited-data conditions frequently suffer from instability and mode collapse. This thesis proposes a novel approach to improve data generation by replacing the traditional random noise input of the generator with structured embeddings (SEs) derived from real data through dimensionality reduction techniques, including Principal Component Analysis (PCA) and Locally Linear Embedding (LLE). Instead of altering the GAN architecture, the proposed method enhances the informativeness of the latent space, providing a meaningful inductive bias for learning. The SEs are integrated into an InfoGAN architecture to leverage disentangled representation learning and support controlled variation. The training strategy using real and generated data further enhances the learning process. Experimental results demonstrate that the proposed method improves generation stability, sample quality, and diversity, while also enhancing robustness under adversarial conditions. These findings highlight the effectiveness of structured latent spaces for stable and semantically coherent generation, providing a practical and comprehensive approach for improving data-driven learning in limited-data industrial environments.