NeurIPS 2019 Disentanglement Challenge: Improved Disentanglement through Aggregated Convolutional Feature Maps
This is an incremental improvement for researchers in disentangled representation learning, as it enhances performance in a specific benchmark challenge.
The paper tackled the problem of improving disentanglement in VAEs for images by proposing a simple preprocessing method using aggregated convolutional feature maps from pretrained CNNs, achieving 2nd place in the NeurIPS 2019 disentanglement challenge.
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.