Search Results for author: Dmitrij Schlesinger

Found 8 papers, 1 papers with code

Symmetric Equilibrium Learning of VAEs

no code implementations19 Jul 2023 Boris Flach, Dmitrij Schlesinger, Alexander Shekhovtsov

We propose a Nash equilibrium learning approach that relaxes these restrictions and allows learning VAEs in situations where both the data and the latent distributions are accessible only by sampling.

Enhancing Fairness of Visual Attribute Predictors

1 code implementation7 Jul 2022 Tobias Hänel, Nishant Kumar, Dmitrij Schlesinger, Mengze Li, Erdem Ünal, Abouzar Eslami, Stefan Gumhold

The performance of deep neural networks for image recognition tasks such as predicting a smiling face is known to degrade with under-represented classes of sensitive attributes.


VAE Approximation Error: ELBO and Exponential Families

no code implementations ICLR 2022 Alexander Shekhovtsov, Dmitrij Schlesinger, Boris Flach

The importance of Variational Autoencoders reaches far beyond standalone generative models -- the approach is also used for learning latent representations and can be generalized to semi-supervised learning.

Crowd Sourcing Image Segmentation with iaSTAPLE

no code implementations21 Feb 2017 Dmitrij Schlesinger, Florian Jug, Gene Myers, Carsten Rother, Dagmar Kainmüller

In an evaluation on a light microscopy dataset containing more than 5000 membrane labeled epithelial cells of a fly wing, we show that iaSTAPLE outperforms STAPLE in terms of segmentation accuracy as well as in terms of the accuracy of estimated crowd worker performance levels, and is able to correctly segment 99% of all cells when compared to expert segmentations.

Image Segmentation Semantic Segmentation

Joint Training of Generic CNN-CRF Models with Stochastic Optimization

no code implementations16 Nov 2015 Alexander Kirillov, Dmitrij Schlesinger, Shuai Zheng, Bogdan Savchynskyy, Philip H. S. Torr, Carsten Rother

We propose a new CNN-CRF end-to-end learning framework, which is based on joint stochastic optimization with respect to both Convolutional Neural Network (CNN) and Conditional Random Field (CRF) parameters.

Stochastic Optimization

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