SuperSimpleNet：统一无监督与监督学习以实现快速可靠的表面缺陷检测
SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection
AI总结：
针对表面缺陷检测中性能、一致性与速度的不足，提出由SimpleNet演变的判别模型SuperSimpleNet，支持无监督与监督学习，在四个基准数据集上达到SOTA。
摘要：
表面缺陷检测旨在识别和定位捕获物体表面的异常区域，这项任务在各行各业的需求日益增长。当前方法往往无法满足这些行业的广泛需求，包括高性能、一致性、快速运行以及利用全部可用训练数据的能力。针对这些差距，我们引入了 SuperSimpleNet，这是一种由 SimpleNet 演变而来的创新判别模型。这一先进模型显著提升了前代模型的训练一致性、推理时间以及检测性能。SuperSimpleNet 仅使用正常训练图像以无监督方式运行，但在有标注的异常训练图像可用时也能从中受益。SuperSimpleNet 在监督和无监督设置下均取得了最先进的结果，这在四个具有挑战性的基准数据集上的实验中得到了证明。代码：https://github.com/blaz-r/SuperSimpleNet 。
英文摘要：
The aim of surface defect detection is to identify and localise abnormal regions on the surfaces of captured objects, a task that's increasingly demanded across various industries. Current approaches frequently fail to fulfil the extensive demands of these industries, which encompass high performance, consistency, and fast operation, along with the capacity to leverage the entirety of the available training data. Addressing these gaps, we introduce SuperSimpleNet, an innovative discriminative model that evolved from SimpleNet. This advanced model significantly enhances its predecessor's training consistency, inference time, as well as detection performance. SuperSimpleNet operates in an unsupervised manner using only normal training images but also benefits from labelled abnormal training images when they are available. SuperSimpleNet achieves state-of-the-art results in both the supervised and the unsupervised settings, as demonstrated by experiments across four challenging benchmark datasets. Code: https://github.com/blaz-r/SuperSimpleNet .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
