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To address this challenge, we propose an iterative inpainting method with a feedback mechanism.
Bayesian inference is used extensively to quantify the uncertainty in an inferred field given the measurement of a related field when the two are linked by a mathematical model.
Spatio-temporal modeling of wireless access latency is of great importance for connected-vehicular systems.
Blind inpainting is a task to automatically complete visual contents without specifying masks for missing areas in an image.
This work introduces a new curriculum-style training approach in the context of image inpainting.
This paper develops a multi-task learning framework that attempts to incorporate the image structure knowledge to assist image inpainting, which is not well explored in previous works.
We investigate the influence of this regularization term on the quality of the generated images and the fulfillment of the given pixel constraints.