異常檢測對於確保電子元件的質量和可靠性有著重要的作用。 在本文中，我們提出一種新的指標梯度平滑值並加入於 F-AnoGAN的損失函數中進行改進。 目標是提高電子元件異常檢測的有效性。
這種方法有助於減少訓練過程中梯度突然波動的影響，這可能導致異常檢測不穩定和不准確。 通過平滑梯度，該模型在識別電子元件異常方面變得更加穩健和可靠。
為了評估修改後的 F-AnoGAN 的有效性，我們使用電子元件圖像數據集進行了實驗。 該數據集由正常和異常樣本組成，代表各種類型的缺陷，例如缺失零件、划痕和未對準的連接器。 我們在數據集上訓練了修改後的 F-AnoGAN，並將其性能與原始 F-AnoGAN 進行了比較。
實驗結果表明，與原始模型相比，改進的 F-AnoGAN 實現了改進的異常檢測性能。 損失函數中加入梯度平滑值，有效減輕了噪聲梯度的影響，增強了模型準確識別電子元件異常的能力。 修改後的 F-AnoGAN 在檢測異常方面表現出更高的精確度、召回率和準確度，從而提高了異常檢測系統的整體可靠性。
從結論得知，所提出的對 F-AnoGAN 損失函數的修改，結合了梯度平滑值，有助於電子元件異常檢測的進步。 這項研究為改善電子行業的質量控制和缺陷檢測流程提供了實際意義。

Anomaly detection plays an important role in ensuring the quality and reliability of electronic components. In this paper, we propose a new indicator gradient smoothing value and add it to the loss function of F-AnoGAN for improvement. The goal is to improve the effectiveness of anomaly detection in electronic components.
This approach helps reduce the impact of sudden gradient fluctuations during training, which can lead to unstable and inaccurate anomaly detection. By smoothing the gradients, the model becomes more robust and reliable in identifying anomalies in electronic components.
To evaluate the effectiveness of the modified F-AnoGAN, we conduct experiments with an electronic component image dataset. The dataset consists of normal and abnormal samples representing various types of defects such as missing parts, scratches, and misaligned connectors. We trained the modified F-AnoGAN on the dataset and compared its performance with the original F-AnoGAN.
Experimental results show that the improved F-AnoGAN achieves improved anomaly detection performance compared to the original model. The gradient smoothing value is added to the loss function, which effectively reduces the influence of the noise gradient and enhances the ability of the model to accurately identify abnormalities in electronic components. The modified F-AnoGAN exhibits higher precision, recall, and accuracy in detecting anomalies, thereby improving the overall reliability of the anomaly detection system.
From the conclusion, the proposed modification of the F-AnoGAN loss function, incorporating gradient smoothing values, contributes to the advancement of electronic component anomaly detection. This research has practical implications for improving quality control and defect detection processes in the electronics industry.

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