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Found 6 papers, 2 papers with code
ImageNet as a Representative Basis for Deriving Generally Effective CNN Architectures
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.
Automated Machine Learning in Practice: State of the Art and Recent Results
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.
DeepScores and Deep Watershed Detection: current state and open issues
This paper gives an overview of our current Optical Music Recognition (OMR) research.
Deep Learning in the Wild
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
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.
DeepScores -- A Dataset for Segmentation, Detection and Classification of Tiny Objects
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.
