Apple has today launched a new Machine Learning blog with a first post on Synthetic Images a method to speed up accuracy and training time. It is all about creating a reference trail and history into how the algorithm interprets and learns from training data.
1. I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, Generative Adversarial Nets. Proceedings Neural Information Processing Systems Conference, 2014.two thousand fourteen
2. X. Zhang, Y. Sugano, M. Fritz, and A. Bulling, Appearance-based Gaze Estimation in the Wild. Proceedings Computer Vision Pattern Recognition Conference, 2015.two thousand fifteen
3. E. Wood, T. Baltrušaitis, L.-P. Morency, P. Robinson, and A. Bulling, Learning an Appearance-based Gaze Estimator from One Million Synthesised Images. Proceedings ACM Symposium on Eye Tracking Research & Applications, 2016.two thousand sixteen
4. P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, Image-to-Image Translation with Conditional Adversarial Networks. ArXiv, 2016.two thousand sixteen
5. J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. ArXiv, 2017.two thousand seventeen
6. M.-Y. Liu, T. Breuel, and J. Kautz, Unsupervised Image-to-Image Translation Networks. ArXiv, 2017.two thousand seventeen
7. P. Costa, A. Galdran, M. I. Meyer, M. D. Abràmoff, M. Niemeijer, A. M.Mendonça, and A. Campilho, Towards Adversarial Retinal Image Synthesis. ArXiv, 2017.two thousand seventeen
8. M. Sela, E. Richardson, and R. Kimmel, Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translation. ArXiv, 2017.two thousand seventeen
9. D. Lee, S.Yun, S. Choi, H. Yoo, M.-H. Yang, and S. Oh, Unsupervised Holistic Image Generation from Key Local Patches. ArXiv, 2017.two thousand seventeen
10. A. Shrivastava, T. Pfister, O. Tuzel, J. Susskind, W. Wang, R. Webb, Learning from Simulated and Unsupervised Images through Adversarial Training. CVPR, 2017.