#Chemdoodle 9.0.3 software#
There is an interesting post on the NextMove Software blog, Just what you wanted for Christmas – a compiler for Gaussian. We show a significant improvement over using synthetic images, and achieve state-of-the-art results on the MPIIGaze dataset without any labeled real data.Įverytime I mention Fortran there is an uptick in the site views so I know there are plenty of readers with an interest in Fortran on a Mac. We quantitatively evaluate the generated images by training models for gaze estimation and hand pose estimation. We show that this enables generation of highly realistic images, which we demonstrate both qualitatively and with a user study. We make several key modifications to the standard GAN algorithm to preserve annotations, avoid artifacts and stabilize training: (i) a 'self-regularization' term, (ii) a local adversarial loss, and (iii) updating the discriminator using a history of refined images. We develop a method for S+U learning that uses an adversarial network similar to Generative Adversarial Networks (GANs), but with synthetic images as inputs instead of random vectors.
To reduce this gap, we propose Simulated+Unsupervised (S+U) learning, where the task is to learn a model to improve the realism of a simulator's output using unlabeled real data, while preserving the annotation information from the simulator.
However, learning from synthetic images may not achieve the desired performance due to a gap between synthetic and real image distributions. With recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations. Apple have published some of their artificial intelligence research, arXiv:1612.07828
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