Synth4Seg -- Learning Defect Data Synthesis for Defect Segmentation using Bi-level Optimization

• URL: http://arxiv.org/abs/2410.18490v1
• Date: Thu, 24 Oct 2024 07:25:12 GMT
• Title: Synth4Seg -- Learning Defect Data Synthesis for Defect Segmentation using Bi-level Optimization
• Authors: Shancong Mou, Raviteja Vemulapalli, Shiyu Li, Yuxuan Liu, C Thomas, Meng Cao, Haoping Bai, Oncel Tuzel, Ping Huang, Jiulong Shan, Jianjun Shi,
• Abstract summary: We propose a bi-level optimization-based synthetic defect data generation framework. We show that the proposed method can be used for learning the most effective locations for pasting synthetic defects. We also demonstrate up to 2.6% performance gain by learning the importance weights for different augmentation-specific defect data sources.
• Score: 37.775768735361275
• License:
• Abstract: Defect segmentation is crucial for quality control in advanced manufacturing, yet data scarcity poses challenges for state-of-the-art supervised deep learning. Synthetic defect data generation is a popular approach for mitigating data challenges. However, many current methods simply generate defects following a fixed set of rules, which may not directly relate to downstream task performance. This can lead to suboptimal performance and may even hinder the downstream task. To solve this problem, we leverage a novel bi-level optimization-based synthetic defect data generation framework. We use an online synthetic defect generation module grounded in the commonly-used Cut\&Paste framework, and adopt an efficient gradient-based optimization algorithm to solve the bi-level optimization problem. We achieve simultaneous training of the defect segmentation network, and learn various parameters of the data synthesis module by maximizing the validation performance of the trained defect segmentation network. Our experimental results on benchmark datasets under limited data settings show that the proposed bi-level optimization method can be used for learning the most effective locations for pasting synthetic defects thereby improving the segmentation performance by up to 18.3\% when compared to pasting defects at random locations. We also demonstrate up to 2.6\% performance gain by learning the importance weights for different augmentation-specific defect data sources when compared to giving equal importance to all the data sources.

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