Evaluation of convolutional neural networks for visual recognition

By Claus Nebauer. 1998

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Citations in the corpus

Multiscale Convolutional Neural Networks for Vision-Based Classification of Cells (2012, background): “Convolutional Neural Networks (CNNs) are multi–layered neural networks that are specialized in pattern recognition tasks [5], [6].”
An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox (2017, background): “Since a global constant learning rate easily causes either a slow convergence with a lower learning rate or a serious ﬂuctuation of the convergence with a higher learning rate, an adaptive learning rate is employed.”
Neocognitron for handwritten digit recognition (2003, background): “Varieties of modi0cations, extensions and applications of the neocognitron and related networks have been reported elsewhere [1,2,10,11,14,16–27].”
Adaptive Convolutional Neural Network and Its Application in Face Recognition (2015, unknown): “[2] presented a hybrid neural network solution for face recognition, which combined SOM and CNN.”
Image processing with neural networks - a review (2002, methodology): “Object recognition based on pixel data Among the ANN approaches developed for pixel-based object recognition [39,42,67,70,72,134–179], several types of ANNs can be distinguished: feed-forward-like ANNs [70,147–149,152,164,165,171,172], variants using weight sharing [144,159,160], recurrent networks ”
A Comprehensive Analysis of Deep Regression (2018, unknown): “For instance, seminal papers exploring efﬁcient back-propagation strategies [27], or evaluating Con-vNets for visual recognition [28], were already published twenty years ago.”