Learning from Simulated and Unsupervised Images through Adversarial Training · HackerPapers discussions

Learning from Simulated and Unsupervised Images through Adversarial Training — mentioned by dimatura

Thu, 18 Jan 2024 12:03:11 GMT

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…

Paper and discussion history. A mention is not an endorsement.

Learning from Simulated and Unsupervised Images through Adversarial Training — mentioned by rgovostes

Mon, 18 May 2020 19:46:04 GMT

Apple's first published ML paper[1] described how they trained a neural network to estimate eye gaze. To augment the limited data set of labeled eye photos, they rendered synthetic eyes and then used adversarial training to make them match the real-life data set. This paper came out several months before the iPhone X introduced Face ID with eye gaze tracking, so it can be seen as a hint of things that were to come. What's intriguing to me is that the paper also talks about doing the same with synthetic depth maps of hands, adding details such as occlusion and imperfectly-captured edges. With…

Paper and discussion history. A mention is not an endorsement.

Learning from Simulated and Unsupervised Images through Adversarial Training — mentioned by alexbeloi

Wed, 01 Nov 2017 03:15:35 GMT

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 * Synthetic avatars…

Paper and discussion history. A mention is not an endorsement.

Learning from Simulated and Unsupervised Images through Adversarial Training — mentioned by iraphael

Sun, 23 Jul 2017 01:07:44 GMT

* Densely Connected Convolutional Networks [0] by Gao Huang, Zhuang Liu, Laurens van der Maaten, & Kilian Q. Weinberger. * Learning from Simulated and Unsupervised Images through Adversarial Training [1] by Ashish Shrivastava, Tomas Pfister, Oncel Tuzel, Joshua Susskind, Wenda Wang, & Russell Webb CVPR 2017 Best Paper Honorable Mention Awards * Annotating Object Instances with a Polygon-RNN [2] by Lluís Castrejón, Kaustav Kundu, Raquel Urtasun, & Sanja Fidler * YOLO9000: Better, Faster, Stronger [3] by Joseph Redmon & Ali Farhadi CVPR 2017 Best Student Paper Award * Computational Imaging on…

Paper and discussion history. A mention is not an endorsement.

Learning from Simulated and Unsupervised Images through Adversarial Training — mentioned by ssivark

Wed, 19 Jul 2017 19:00:48 GMT

It is not an anonymous publication! 1. The article is based on a paper submitted to the prestigious CVPR, which is obviously not anonymous. ( https://arxiv.org/abs/1612.07828 ) They mention this at the end of the post. 2. The blog's "journal" name is misleading. It means "journal" in the sense of a record of some of the stuff happening at Apple. My guess is that the blog post does not name authors because it was written by an Apple representative who manages public communications---probably because Apple is very very particular about managing their brand and what they say publicly. I wouldn't…

Paper and discussion history. A mention is not an endorsement.

Learning from Simulated and Unsupervised Images through Adversarial Training — mentioned by zo7

Wed, 19 Jul 2017 17:02:38 GMT

It's interesting how much criticism they're getting because Apple formatted their blog to be anonymous and watered down, but they're clear in their first technical post that it is just an overview of work that the researchers are presenting at CVPR [1]. So the researchers at Apple are still getting credit for their work in the scientific community, but the PR-facing side of their work is anonymous, probably for some aesthetic reason (this is Apple, of course) [1]: https://arxiv.org/abs/1612.07828

Paper and discussion history. A mention is not an endorsement.

Learning from Simulated and Unsupervised Images through Adversarial Training — mentioned by Stasis5001

Wed, 19 Jul 2017 15:47:36 GMT

A lot of academic papers actually aren't all that great, for a variety of reasons. Normally, you can use citations, journal, and author credentials to get a sense of whether a paper is even worth skimming. The only "paper" on the "journal" right now looks like it's just a watered-down, html-only version of https://arxiv.org/abs/1612.07828! Seems like more of a PR stunt than anything useful, but who knows.

Paper and discussion history. A mention is not an endorsement.

Learning from Simulated and Unsupervised Images through Adversarial Training — mentioned by JamilD

Mon, 26 Dec 2016 14:06:19 GMT

The link to the paper itself, for those who want to skip the Forbes article: https://arxiv.org/abs/1612.07828

Paper and discussion history. A mention is not an endorsement.

Learning from Simulated and Unsupervised Images through Adversarial Training — HN submission

Mon, 26 Dec 2016 07:56:59 GMT

Learning from Simulated and Unsupervised Images through Adversarial Training

103 points; 9 discussion replies on HN. Paper and source history. A submission is not an endorsement.