Learning from Simulated and Unsupervised Images through Adversarial Training
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Re: Learning from Simulated and Unsupervised Images through Adversarial Training
#2FYI, the title has been changed from this being the first publication by Apple on arXiv to the title of the paper.
It seems the technique presented in the paper was simultaneously discovered by Sixt et al: https://arxiv.org/abs/1611.01331 (Nov. 4th vs Nov. 15th)
Re: Learning from Simulated and Unsupervised Images through Adversarial Training
#3[deleted]
Re: Learning from Simulated and Unsupervised Images through Adversarial Training
#4Yay! Apple publishing ML papers. The paper's pretty interesting in concept, but I would have loved to see more variety of samples.
If we could make rendered images more natural, wouldn't that have a huge benefit on gaming? We could make games almost identical to real video/images. Couple that to 3D VR headset and you have a pretty sweet system.
Re: Learning from Simulated and Unsupervised Images through Adversarial Training
#5This is the First ever Machine Learning Paper from !
Re: Learning from Simulated and Unsupervised Images through Adversarial Training
#6This is the First ever Machine Learning Paper from !
\uF8FF?
EDIT: ah, that's where Apple puts their logo in their fonts... that is private unicode space, so don't expect it to show up anywhere non-Apple.
Re: Learning from Simulated and Unsupervised Images through Adversarial Training
#7OP here, not sure who has changed the title. Thought it was relevant to the paper.
Original title: First publication from Apple: ...
Re: Learning from Simulated and Unsupervised Images through Adversarial Training
#8I found the paper very well written. It's a testament to Andrew Ng's ML class that you're able to follow a state of the art paper (terms, equations, conventions) after his lessons.
The notion of a "visual Turing test" was interesting (can humans distinguish real from simulated images). The sample sizes (and p-values, for all their issues) were fairly small (10 participants, 100 images each).
I wonder if there are volunteer participant efforts (similar to HealthKit) to help automate these types of evaluations. You could even have a Recaptcha-style service to show your simulations are realistic enough.
Re: Learning from Simulated and Unsupervised Images through Adversarial Training
#9This paper is important for a few reasons:
- Labeled data is very expensive. Historically attempts to learn on synthetic data has failed because ConvNets are very good at detecting small visual artifacts in the synthetic data and using those for classification during training. At test time on real data, those artifacts aren't present so model fails. A technique that can beat state-of-the-art (admittedly on a very narrow Eye Gaze dataset, but still) by only training on labels from synthetic data and testing on real data is important.
- They present a useful new idea to improve GAN training: using a history of "fake" images, rather than only the latest fake images from the generator. Ask anyone who has tried to train a GAN: the training is really unstable, each network only cares about beating the latest version of its "opponent". They show good improvements by saving many previous fake outputs to make the generator more robust. This reminds me of Experience Replay from DeepMind for RL.
- It's a published paper from Apple! Great that they are starting to contribute back to the research community.
Re: Learning from Simulated and Unsupervised Images through Adversarial Training
#10OP here, not sure who has changed the title. Thought it was relevant to the paper. Original title: First publication from Apple: ...
A moderator changed the title from “Apple's gift for christmas: An actual AI paper”, which breaks the submission guidelines by editorializing:
> Otherwise please use the original title, unless it is misleading or linkbait.