Search Results for author: Thomas Wollmann

Found 5 papers, 1 papers with code

MEAL: Manifold Embedding-based Active Learning

no code implementations22 Jun 2021 Deepthi Sreenivasaiah, Johannes Otterbach, Thomas Wollmann

Active learning helps learning from small amounts of data by suggesting the most promising samples for labeling.

Active Learning Autonomous Driving +3

DAAIN: Detection of Anomalous and Adversarial Input using Normalizing Flows

1 code implementation30 May 2021 Samuel von Baußnern, Johannes Otterbach, Adrian Loy, Mathieu Salzmann, Thomas Wollmann

We demonstrate the effectiveness of our approach using an ESPNet trained on the Cityscapes dataset as segmentation model, an affine Normalizing Flow as density estimator and use blue noise to ensure homogeneous sampling.

Image Segmentation Segmentation +1

Chameleon: A Semi-AutoML framework targeting quick and scalable development and deployment of production-ready ML systems for SMEs

no code implementations8 May 2021 Johannes Otterbach, Thomas Wollmann

Developing, scaling, and deploying modern Machine Learning solutions remains challenging for small- and middle-sized enterprises (SMEs).

AutoML BIG-bench Machine Learning

Automatic breast cancer grading in lymph nodes using a deep neural network

no code implementations24 Jul 2017 Thomas Wollmann, Karl Rohr

We describe a novel method for effectively performing classification of whole-slide images and patient level breast cancer grading.

Classification General Classification +1

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