A bit of a tangent, but reminds me of this article, RenderGAN [1]. The main point of the article was about using 3D rendering + GAN to generate convincing synt

A bit of a tangent, but reminds me of this article, RenderGAN [1]. The main point of the article was about using 3D rendering + GAN to generate convincing synthetic data, and their application was creating training data to detect "barcodes" [2] attached to bees. It's a similar premise and method than Apple's SUGAN paper [3], that came out around the same time, to much more fanfare (it won CVPR best paper award iirc). I think the impact of renderGAN was a bit limited by the narrow scope of their application (even narrower than Apple's eye tracker application) and the fact that the generator had some handcrafted layers that didn't really generalize well outside the black and white marker application. [1] https://www.frontiersin.org/articles/10.3389/frobt.2018.00066/full https://www.frontiersin.org/articles/10.3389/frobt.2018.0006... [2] Technically not a barcode since it doesn't have bars, it's a circular 2D marker, but close enough. For that matter the QR-like codes aren't really "bar"codes either, but that's just pedantry. [3] https://arxiv.org/abs/1612.07828 https://arxiv.org/abs/1612.07828