论文标题
使用VAE和Beta-Vae无监督的异常定位
Unsupervised anomaly localization using VAE and beta-VAE
论文作者
论文摘要
变异自动编码器（VAE）在数据分布的无监督学习中表现出巨大的潜力。预计对正常图像进行训练的VAE只能重建正常图像，从而可以通过操纵VAE Elbo损失中的信息在图像中定位异常像素。 ELBO由KL差异损失（图像）和重建损失（Pixel Wise）组成。以后用作预测因子是自然而直接的。但是，通常添加到正常图像中的局部异常会使整个重建图像恶化，从而仅使用幼稚像素误差不准确地导致分割。提出了基于能量的投影，以提高正常区域/像素的重建精度，从而实现了简单自然图像的最新定位精度。另一个可能的预测因素是Elbo及其组件梯度相对于每个像素。先前的工作声称KL梯度是一个强大的预测指标。在本文中，我们认为医学成像中基于能量的投影不如自然图像有用。此外，我们观察到KL梯度预测变量的鲁棒性完全取决于VAE和数据集的设置。我们还探讨了β-VAE内KL损失的重量和异常定位中预测器集合的影响。
Variational Auto-Encoders (VAEs) have shown great potential in the unsupervised learning of data distributions. An VAE trained on normal images is expected to only be able to reconstruct normal images, allowing the localization of anomalous pixels in an image via manipulating information within the VAE ELBO loss. The ELBO consists of KL divergence loss (image-wise) and reconstruction loss (pixel-wise). It is natural and straightforward to use the later as the predictor. However, usually local anomaly added to a normal image can deteriorate the whole reconstructed image, causing segmentation using only naive pixel errors not accurate. Energy based projection was proposed to increase the reconstruction accuracy of normal regions/pixels, which achieved the state-of-the-art localization accuracy on simple natural images. Another possible predictors are ELBO and its components gradients with respect to each pixels. Previous work claimed that KL gradient is a robust predictor. In this paper, we argue that the energy based projection in medical imaging is not as useful as on natural images. Moreover, we observe that the robustness of KL gradient predictor totally depends on the setting of the VAE and dataset. We also explored the effect of the weight of KL loss within beta-VAE and predictor ensemble in anomaly localization.
