30 citations · 33 across the 4 of their papers we have counts for
4 papers
Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels
Valentyn Melnychuk, Evgeniy Faerman, Ilja Manakov +1
Unlabeled data is often abundant in the clinic, making machine learning methods based on semi-supervised learning a good match for this setting. Despite this, they are currently re…
Walking the Tightrope: An Investigation of the Convolutional Autoencoder Bottleneck
Ilja Manakov, Markus Rohm, Volker Tresp
In this paper, we present an in-depth investigation of the convolutional autoencoder (CAE) bottleneck. Autoencoders (AE), and especially their convolutional variants, play a vital…
Push it to the Limit: Discover Edge-Cases in Image Data with Autoencoders
Ilja Manakov, Volker Tresp
In this paper, we focus on the problem of identifying semantic factors of variation in large image datasets. By training a convolutional Autoencoder on the image data, we create en…
Noise as Domain Shift: Denoising Medical Images by Unpaired Image Translation
Ilja Manakov, Markus Rohm, Christoph Kern +3
We cast the problem of image denoising as a domain translation problem between high and low noise domains. By modifying the cycleGAN model, we are able to learn a mapping between t…