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Physically-admissible polarimetric data augmentation for road-scene analysis
Polarimetric imaging, along with deep learning, has shown improved performances on different tasks including scene analysis. However, …
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Pixel-wise Conditioned Generative Adversarial Networks for Image Synthesis and Completion
We address the task of learning effective generative adversarial networks when only very few pixel values are known beforehand.
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Gradient-based deterministic inversion of geophysical data with generative adversarial networks: Is it feasible?
We study the use of GANs for gradient-based deterministic inversion in Geophysics and highlight the associated difficulties caused by the nonlinear GAN transform.
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Pixel-wise Conditioning of Generative Adversarial Networks
We investigate adding a regularization term to the conditioning of GANs to deal with cases where a small subset of the image is known beforehand.
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Dilated Spatial Generative Adversarial Networks for Ergodic Image Generation
We have presented an architecture for globally ergodic data generation. We have shown that our method produces samples that are sharper and less noisy than the previous approaches, removing the need for ad-hoc solutions like median filters, without altering their quality.
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