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Found 6 papers, 1 papers with code
Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders
Our non-adversarial approach is in contrast with the recent works that combine adversarial training with auto-encoders to disentangle representations.
Adversarially Tuned Scene Generation
Although simulated data augmented with a few real world samples has been shown to mitigate domain shift and improve transferability of trained models, guiding or bootstrapping the virtual data generation with the distributions learnt from target real world domain is desired, especially in the fields where annotating even few real images is laborious (such as semantic labeling, and intrinsic images etc.).
Model-driven Simulations for Deep Convolutional Neural Networks
The use of simulated virtual environments to train deep convolutional neural networks (CNN) is a currently active practice to reduce the (real)data-hungriness of the deep CNN models, especially in application domains in which large scale real data and/or groundtruth acquisition is difficult or laborious.
Cardiac Motion Analysis by Temporal Flow Graphs
The paper proposes a new methodology for cardiac motion analysis based on the temporal behaviour of points of interest on the myocardium.
Model Validation for Vision Systems via Graphics Simulation
We adapt the methodology in the context of current graphics simulation tools for modeling data generation processes and, for systematic performance characterization and trade-off analysis for vision system design leading to qualitative and quantitative insights.
Simulations for Validation of Vision Systems
As the computer vision matures into a systems science and engineering discipline, there is a trend in leveraging latest advances in computer graphics simulations for performance evaluation, learning, and inference.
