论文标题
将gan和自动编码器结合起来，以进行有效的异常检测
Combining GANs and AutoEncoders for Efficient Anomaly Detection
论文作者
论文摘要
在这项工作中，我们提出了CBIGAN - 一种在图像中检测异常检测的新方法，其中一致性约束是在Bigan的编码器和解码器中作为正则化项引入的。我们的模型具有相当好的建模能力和重建一致性能力。我们在MVTEC AD上评估了所提出的方法 - 高分辨率图像上无监督异常检测的现实基准测试 - 并将其与标准基线和最先进的方法进行比较。实验表明，所提出的方法通过很大的边距提高了Bigan配方的性能，并且与昂贵的最新迭代方法相当，同时降低了计算成本。我们还观察到，我们的模型在纹理型异常检测中特别有效，因为它在此类别中设定了新的最新状态。我们的代码可在https://github.com/fabiocarrara/cbigan-ad/上找到。
In this work, we propose CBiGAN -- a novel method for anomaly detection in images, where a consistency constraint is introduced as a regularization term in both the encoder and decoder of a BiGAN. Our model exhibits fairly good modeling power and reconstruction consistency capability. We evaluate the proposed method on MVTec AD -- a real-world benchmark for unsupervised anomaly detection on high-resolution images -- and compare against standard baselines and state-of-the-art approaches. Experiments show that the proposed method improves the performance of BiGAN formulations by a large margin and performs comparably to expensive state-of-the-art iterative methods while reducing the computational cost. We also observe that our model is particularly effective in texture-type anomaly detection, as it sets a new state of the art in this category. Our code is available at https://github.com/fabiocarrara/cbigan-ad/.
