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A deep learning approach for detection and localization of leaf anomalies
The detection and localization of possible diseases in crops are usually automated by resorting to supervised deep learning approaches.
Hierarchical model reduction driven by machine learning for parametric advection-diffusion-reaction problems in the presence of noisy data
We propose a new approach to generate a reliable reduced model for a parametric elliptic problem, in the presence of noisy data.
Anisotropic mesh adaptation for region-based segmentation accounting for image spatial information
The methodology relies on a split Bregman algorithm for the minimisation of a region-based energy functional and on an anisotropic recovery-based error estimate to drive mesh adaptation.
Scalable Balanced Training of Conditional Generative Adversarial Neural Networks on Image Data
We propose a distributed approach to train deep convolutional generative adversarial neural network (DC-CGANs) models.
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