Generative Adversarial Networks · HackerPapers discussions

Generative Adversarial Networks — mentioned by ml_basics

Thu, 26 Sep 2024 05:23:28 GMT

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 (https://arxiv.org/abs/1406.2661) The amount of progress we've made is mind boggling.

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Generative Adversarial Networks — mentioned by godelski

Sun, 25 Feb 2024 18:59:25 GMT

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 (the work that kicked off the diffusion model era) was published in 2015[0], only a year after GANs[1]. Diffusion then showed promise but why did DDPM come out in 2020[2]? Because everyone was working on GANs. GANs produced far better images and diffusion (still is) was a lot more computationally intensive. Plus, all…

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Generative Adversarial Networks — mentioned by godelski

Wed, 29 Nov 2023 20:15:52 GMT

> 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 much easier to do now, and 2) the computational requirements and (more importantly) technical skills have dramatically dropped. We should also be explicitly aware that deep fakes are still new. GANs in 2014 were not creating high definition images. They were doing fuzzy black and white 28x28 faces, poorly, and 32x32…

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Generative Adversarial Networks — HN submission

Tue, 30 May 2023 20:51:18 GMT

Generative Adversarial Networks

21 points; 3 discussion replies on HN. Paper and source history. A submission is not an endorsement.

Generative Adversarial Networks — mentioned by lwneal

Thu, 25 May 2023 18:16:07 GMT

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], the Krizhevsky ImageNet paper [4], the original GAN [5], and arguably also the AlphaGo paper [6] and the Atari DQN paper [7]. [1] https://aima.cs.berkeley.edu/ [2] https://www.deeplearningbook.org/ [3] https://www.amazon.com/Probabilistic-Graphical-Models-Princi... [4]…

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Generative Adversarial Networks — mentioned by vikashrungta

Wed, 08 Feb 2023 16:19:46 GMT

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 You Need" (2017) - https://arxiv.org/abs/1706.03762 (Google Brain) 2. "Generative Adversarial Networks" (2014) - https://arxiv.org/abs/1406.2661 (University of Montreal) 3. "Dynamic Routing Between Capsules" (2017) - https://arxiv.org/abs/1710.09829 (Google Brain) 4. "Unsupervised Representation Learning with Deep…

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Generative Adversarial Networks — mentioned by CGamesPlay

Fri, 27 Jan 2023 12:01:39 GMT

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

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Generative Adversarial Networks — mentioned by phonebucket

Tue, 24 Jan 2023 15:29:18 GMT

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 and networks etc. 2) Neural Style Transfer (https://arxiv.org/abs/1508.06576). Nice to be able to manipulate pretrained networks and intercept intermediate layers. 3) Deep Image Prior (https://arxiv.org/abs/1711.10925). Nice low-data exercise in building out an autoencoder. 4) Physics Informed Neural Networks…

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Generative Adversarial Networks — mentioned by skunkworker

Sat, 07 May 2022 21:08:28 GMT

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

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Generative Adversarial Networks — mentioned by somethingAlex

Thu, 15 Apr 2021 18:55:28 GMT

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 "World Model" and "Controller" instead of the more general "Discriminator" and "Generator." In this paper, he says the GAN is a more specific application of his Artificial Curiosity paper, but I see it the other way around. Goodfellow should have cited the Artificial Curiosity paper though, as training two networks at…

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Generative Adversarial Networks — mentioned by lwneal

Wed, 17 Jun 2020 00:42:07 GMT

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! You might be interested to know that the technological breakthrough that made today's realistic image and audio generation possible was in fact a form of this very idea: one AI creates fake but realistic data, while another AI attempts to detect the fake data. The two systems fight against each other in a game…

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Generative Adversarial Networks — mentioned by crucialfelix

Thu, 25 Oct 2018 21:58:26 GMT

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 changed in the early 20th century. I really feel bad for Dr. Beef, this must really hurt. But according to the twitter thread ( https://twitter.com/DrBeef_/status/1055285640420483073 ) they didn't use the pre-trained models. In any case pre-trained models are labor, not end product. They simply trained them from scratch.…

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Generative Adversarial Networks — mentioned by whataretensors

Sat, 19 May 2018 17:55:35 GMT

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

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Generative Adversarial Networks — mentioned by ttul

Sun, 22 Apr 2018 15:08:43 GMT

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

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Generative Adversarial Networks — mentioned by vinn124

Thu, 04 Jan 2018 22:10:33 GMT

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

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Generative Adversarial Networks — mentioned by vinn124

Thu, 04 Jan 2018 22:01:54 GMT

> 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 0.1 or -0.5, which is within one standard deviation of the mean of 0); you get -1 if you predict a number thats "far" from this distribution (something like 40 - which has an infinitesimally low probability of being drawn from a unit gaussian). mode collapse, then, is when you predict 0 all the time. yes, you are…

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Generative Adversarial Networks — mentioned by whataretensors

Thu, 24 Aug 2017 16:20:28 GMT

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

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Generative Adversarial Networks — mentioned by robryk

Mon, 24 Apr 2017 19:37:51 GMT

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

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Generative Adversarial Networks — mentioned by itchyjunk

Fri, 17 Mar 2017 23:02:28 GMT

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 [2] https://github.com/yahoo/open_nsfw

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Generative Adversarial Networks — mentioned by westoncb

Sun, 15 Jan 2017 23:38:45 GMT

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

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Generative Adversarial Networks — mentioned by westoncb

Sun, 15 Jan 2017 23:29:32 GMT

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, and the other by getting better at generating. That basic architecture is quite general—which I think is why you also see e.g. LeCun being excited about this direction. To be fair, the article does a kind of weird thing in discussing three different ways game theory has shown up in the context of machine learning…

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Generative Adversarial Networks — HN submission

Sun, 06 Nov 2016 19:54:29 GMT

Generative Adversarial Nets [pdf]

1 point; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

Generative Adversarial Networks — mentioned by m0g

Tue, 13 Oct 2015 12:23:44 GMT

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

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Generative Adversarial Networks — mentioned by thisisdave

Thu, 19 Jun 2014 18:45:07 GMT

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 thought it was really interesting. http://arxiv.org/abs/1406.2661

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