Generative Adversarial Networks

It's quite remarkable how much the goal posts have shifted when it comes to what is impressive with AI/ML. Things like this are a good reminder. 10 years ago the GAN paper came out and everyone was excited how amazing the generated image quality was…

ml_basicsSep 26, 2024View on HNParent context

Yes and no. The thing is that Mamba is not perfect. There's no neural architecture to rule them all, if you will. I think the bigger issue is that we more act like there is and get on bandwagons. Let me give a clearer example from the past so we can see. The predecessor to DDPM…

godelskiFeb 25, 2024View on HNParent context

> I'm disgusted, but not surprised, that teenage kids are generating CSAM like this. Even before we had diffusion models, we had GANs and deepfakes, which were almost immediately used for generating shittons of nonconsensual porn I think the big difference now is that 1) it's…

godelskiNov 29, 2023View on HNParent context

This is a fine list, but it only covers a specific type of generative AI. Any set of resources about AI in general has to at least include the truly canonical Norvig & Russel textbook [1]. Probably also canonical are Goodfellow's Deep Learning [2], Koller & Friedman's PGMs [3]…

lwnealMay 25, 2023View on HNParent context

I posted a list of papers on twitter, and will be posting a summary for each of them as well. here is the list https://twitter.com/vrungta/status/1623343807227105280 Unlocking the Secrets of AI: A Journey through the Foundational Papers by @vrungta (2023) 1. "Attention is All…

vikashrungtaFeb 8, 2023View on HNParent context

The arms race was first automated in 2014, when the GAN paper was published. That's what GANs do: half the network generates garbage, the other half identifies if it was garbage or real. https://arxiv.org/abs/1406.2661

CGamesPlayJan 27, 2023View on HNParent context

A lot depends on what you're interested in. Some papers that are runnable on a laptop CPU (so long as you stick to small image sizes/tasks): 1) Generative Adversarial Networks (https://arxiv.org/abs/1406.2661). Good practice to have a custom training loops, different optimisers…

phonebucketJan 24, 2023View on HNParent context

Quite a big loss for Apple. Ian was the primary author of the 2014 GAN paper. https://arxiv.org/abs/1406.2661

skunkworkerMay 7, 2022View on HNParent context

With regards to his claim that he described a GAN before Ian Goodfellow did, it certainly seems like that is the case. The problem is that, in his original Artificial Curiosity paper he talks so specifically about world-simulating reinforcement learning. He even uses the terms…

somethingAlexApr 15, 2021View on HNParent context

This article talks about technologies used to produce human-like text, and "deep-fake" video and audio. The article argues that such generative AI can be used by criminals to commit fraud- but at the same time, AI can also be used on the other side to detect and prevent fraud!…

lwnealJun 17, 2020View on HNParent context

The work of Art is the act of putting it up for auction on Christie's. That's what makes it a conceptual piece that is being discussed. People often mistake art for a form of labor production. They expect that labor has to have been performed to create value. But that all…

crucialfelixOct 25, 2018View on HNParent context

Starring Goodfellow et al https://arxiv.org/abs/1406.2661

whataretensorsMay 19, 2018View on HNParent context

Read the original GAN paper: https://arxiv.org/abs/1406.2661

ttulApr 22, 2018View on HNParent context

sounds like a job for [adversarial networks](https://arxiv.org/pdf/1406.2661.pdf)!

vinn124Jan 4, 2018View on HNParent context

> but isn't that the whole point of GANs? not quite, but youre on the right path. think about it this way: you (the generative model) are trying to predict a unit gaussian, which is just a fancy way to say bell curve. you get +1 if you predict a number in this distribution (eg…

vinn124Jan 4, 2018View on HNParent context

The original GAN paper was pretty big for me. https://arxiv.org/pdf/1406.2661.pdf

whataretensorsAug 24, 2017View on HNParent context

This is likely part of how the speech-generating NNs are trained (ie. there's a generated-speech-detector and the network is trained to fool it, while it is also trained): https://arxiv.org/abs/1406.2661

robrykApr 24, 2017View on HNParent context

So this is to counter stuff like GAN's (generative adversarial networks) [1] to reverse engineer data out of a black box systems? Like Yahoo's NSFW [2] classifier for example. [1] https://en.wikipedia.org/wiki/Generative_adversarial_network... [1] https://arxiv.org/abs/1406.2661…

itchyjunkMar 17, 2017View on HNParent context

My understanding is that the use of game theory in this context has nothing to do with the domain in which the network is being trained. It's not about creating game playing AI's—it's about a 'game' between two neural networks, one who competes by getting better at classifying…

westoncbJan 15, 2017View on HNParent context

So is [1], for example, misleading? [1] https://arxiv.org/abs/1406.2661

westoncbJan 15, 2017View on HNParent context

your idea is called adversarial networks, and it works quite well (http://arxiv.org/abs/1406.2661) although it's not useful every time.

m0gOct 13, 2015View on HNParent context

An overlapping group of authors (compared with the ones that wrote the "intriguing properties" paper) recently published a neat preprint on using adversarial networks as part of the training process. The goals of the paper are different from the networks discussed here, but I…

thisisdaveJun 19, 2014View on HNParent context