论文标题
Neurips 2019 DISENTANGREMT挑战：通过汇总卷积特征图改进了分解
NeurIPS 2019 Disentanglement Challenge: Improved Disentanglement through Aggregated Convolutional Feature Maps
论文作者
论文摘要
与直接使用图像相比，该报告向我们提交给Neurips 2019 Distangrement挑战的1阶段提交挑战提出了一种简单的图像预处理方法，可用于训练VAE，从而改善了分解的方法。特别是，我们建议使用从ImageNet预测的CNN中提取的区域聚合特征图。我们的方法在挑战的第1阶段中获得了第二名。代码可从https://github.com/mseitzer/neurips2019-disentanglement-challenge获得。
This report to our stage 1 submission to the NeurIPS 2019 disentanglement challenge presents a simple image preprocessing method for training VAEs leading to improved disentanglement compared to directly using the images. In particular, we propose to use regionally aggregated feature maps extracted from CNNs pretrained on ImageNet. Our method achieved the 2nd place in stage 1 of the challenge. Code is available at https://github.com/mseitzer/neurips2019-disentanglement-challenge.
