3 papers
cs.LG2020
NeurIPS 2019 Disentanglement Challenge: Improved Disentanglement through Aggregated Convolutional Feature Maps
Maximilian Seitzer
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 disentangleme…
cs.LG2020
NeurIPS 2019 Disentanglement Challenge: Improved Disentanglement through Learned Aggregation of Convolutional Feature Maps
Maximilian Seitzer, Andreas Foltyn, Felix P. Kemeth
This report to our stage 2 submission to the NeurIPS 2019 disentanglement challenge presents a simple image preprocessing method for learning disentangled latent factors. We propos…
cs.CV2018
Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction
Maximilian Seitzer, Guang Yang, Jo Schlemper +9
Deep learning approaches have shown promising performance for compressed sensing-based Magnetic Resonance Imaging. While deep neural networks trained with mean squared error (MSE)…