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ICCV 2017 this year had dozens of papers using GANs and other methods to make simulated images/videos look more realistic. The hunger for cheap usable data is r

• kalal 9y ago
  Can you provide some links? this sounds interesting ...
  • alexbeloi 9y ago
    I spoke in hyperbole, there were dozens of papers using generative networks to create real looking simulated data which is the more common approach. You start with noise + some features you want in your image, then you turn that into a realistic image. The notable exception is if you want to generate segmentation data (where you want labels for each pixel), in this case the set of features you want is essentially an image already. ICCV 2017 * Photographic Image Synthesis with Cascaded Refinement Networks: https://arxiv.org/pdf/1707.09405.pdf https://arxiv.org/pdf/1707.09405.pdf * Synthetic avatars: https://arxiv.org/pdf/1704.05693.pdf https://arxiv.org/pdf/1704.05693.pdf * Synthesizing cell microscopy data: https://arxiv.org/pdf/1708.04692.pdf https://arxiv.org/pdf/1708.04692.pdf * Material Editing Using a Physically Based Rendering Network: https://arxiv.org/pdf/1708.00106.pdf https://arxiv.org/pdf/1708.00106.pdf * 3D-PRNN: Generating Shape Primitives With Recurrent Neural Networks: * for more search 'generative' 'generating' 'simulating' 'synthesis' 'synthetic' on: http://iccv2017.thecvf.com/program/main_conference#schedule http://iccv2017.thecvf.com/program/main_conference#schedule Also worth mentioning: CVPR 2017 best paper, this is the other exception, it's unique because it doesn't require _any_ labeled data * Learning from Simulated and Unsupervised Images through Adversarial Training: https://arxiv.org/abs/1612.07828 https://arxiv.org/abs/1612.07828