To train the model efficiently on noisy data, we propose a self-adversarial learning method and a cascade training method.
To address these issues, we construct new graph models to represent the contextual information of each node and the long-term spatio-temporal data dependency structure.
The trained GPR model encodes the nonlinearities and anisotropies present in the microscale and serves as a material model for the membrane response of the macroscale shell.
Unlike the previous OCR agnostic preprocessing techniques, the proposed approach approximates the gradient of a particular OCR engine to train a preprocessor module.
Blur detection is the separation of blurred and clear regions of an image, which is an important and challenging task in computer vision.