Search Results for author: Lukas Tuggener

Found 6 papers, 2 papers with code

ImageNet as a Representative Basis for Deriving Generally Effective CNN Architectures

no code implementations16 Mar 2021 Lukas Tuggener, Jürgen Schmidhuber, Thilo Stadelmann

We investigate and improve the representativeness of ImageNet as a basis for deriving generally effective convolutional neural network (CNN) architectures that perform well on a diverse set of datasets and application domains.

Image Classification

Automated Machine Learning in Practice: State of the Art and Recent Results

no code implementations19 Jul 2019 Lukas Tuggener, Mohammadreza Amirian, Katharina Rombach, Stefan Lörwald, Anastasia Varlet, Christian Westermann, Thilo Stadelmann

A main driver behind the digitization of industry and society is the belief that data-driven model building and decision making can contribute to higher degrees of automation and more informed decisions.

AutoML Decision Making

Deep Learning in the Wild

no code implementations13 Jul 2018 Thilo Stadelmann, Mohammadreza Amirian, Ismail Arabaci, Marek Arnold, Gilbert François Duivesteijn, Ismail Elezi, Melanie Geiger, Stefan Lörwald, Benjamin Bruno Meier, Katharina Rombach, Lukas Tuggener

Deep learning with neural networks is applied by an increasing number of people outside of classic research environments, due to the vast success of the methodology on a wide range of machine perception tasks.

Deep Watershed Detector for Music Object Recognition

no code implementations26 May 2018 Lukas Tuggener, Ismail Elezi, Jurgen Schmidhuber, Thilo Stadelmann

Optical Music Recognition (OMR) is an important and challenging area within music information retrieval, the accurate detection of music symbols in digital images is a core functionality of any OMR pipeline.

Information Retrieval Music Information Retrieval +2

DeepScores -- A Dataset for Segmentation, Detection and Classification of Tiny Objects

2 code implementations27 Mar 2018 Lukas Tuggener, Ismail Elezi, Jürgen Schmidhuber, Marcello Pelillo, Thilo Stadelmann

We present the DeepScores dataset with the goal of advancing the state-of-the-art in small objects recognition, and by placing the question of object recognition in the context of scene understanding.

General Classification Object Recognition +2

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