Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks · HackerPapers discussions

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks — HN submission

Wed, 15 Jul 2026 09:01:57 GMT

Unsupervised Representation Learning with Deep Convolutional GANs

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

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks — mentioned by swyx

Wed, 06 Sep 2023 18:31:13 GMT

i highlighted this last night which seems to be making the rounds - https://twitter.com/swyx/status/1699369076529971545 Alec's CV (https://www.linkedin.com/in/alecradford/) seems to be: - 2011-2016 BSc Eng from Olin College - 2013+ started a data/AI consultancy as a sophomore that turned into a vague startup/product? - 2015 first paper on GANs coauthored with soumith https://arxiv.org/pdf/1511.06434.pdf%C3 - 2016 first GAN paper under openai email https://proceedings.neurips.cc/paper_files/paper/2016/file/8... - 2017 the generative reviews paper mentioend in the Wired article, with Ilya…

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Unsupervised Representation Learning with Deep Convolutional 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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Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks — mentioned by deepnet

Mon, 10 Jul 2017 17:58:43 GMT

Geoff Hinton refers to thought vectors performing reasoning by analogy using algebra [1] in his Royal Society Lecture. The other widely reported vector algebras in a semantic space were discovered by Mikolov et al when producing ~300 dimensional vectors for a billion word Wikipedia corpus. If one performs vector algebra and ~= is near by cosine distance then using Mikolov's Vectors[3]. King - Man + Woman ~= Queen France - Paris + Gernmany ~= Berlin Surprisingly this works for other modalities, Chintala, Radford & Metz found a latent semantic space in images, that adds vectors for glasses or…

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Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks — mentioned by vintermann

Fri, 17 Jun 2016 07:31:44 GMT

Yup, that was the original DCGAN paper: https://arxiv.org/abs/1511.06434

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Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks — mentioned by zodiac

Fri, 03 Jun 2016 14:20:51 GMT

> But what's the point? Autoencoders don't really have much practical use now, but that's not the point of them. The point is we really want to figure out how to do unsupervised learning well and autoencoders are one of the few ways of doing it. We want to do unsupervised learning well because most learning that humans do is unsupervised. The idea behind autoencoding is that by forcing the network to try to learn efficient ways to compress the data, it could learn important features of the data. The fact that the pictures are so blurry means that this doesn't work very well, but that's why…

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Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks — mentioned by arketyp

Fri, 08 Apr 2016 04:03:38 GMT

I'm guessing recent progress in texture synthesis [1] and content generation [2] using deep convolutional nets made this possible. It would be interesting to see what was actually fed into the generating system. That would be a piece of art truly speaking magnitudes. [1] http://arxiv.org/abs/1505.07376 [2] http://arxiv.org/abs/1511.06434

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Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks — mentioned by jkldotio

Wed, 23 Mar 2016 23:12:08 GMT

I don't speak with any authority on this. With word2vec you can do operations like king - man + woman and the nearest vector will be queen. Similarly capital cities will be in a similar relationship to their countries so if you take Rome - Italy + Spain you will be nearest to Madrid. That permits limited reasoning by analogy and there are some analogy benchmark tasks which those word models perform reasonably well on, effectively answering loads of questions like "Madrid is to Spain as Rome is to ____?". This model is more complex and seems to be using a series of techniques to "disentangle"…

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Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks — mentioned by Sergej-Shegurin

Mon, 14 Mar 2016 16:19:13 GMT

(1) Adversarial learning is unsupervised and works great. Most of language modeling is unsupervised (you predict next word, but it's not real supervision because it's self-supervision). There're many works in computer vision which are unsupervised and still give more or less reasonable performance. See f.e. http://arxiv.org/pdf/1511.05045v2.pdf for unsupervised learning in action recognition, also http://arxiv.org/pdf/1511.06434v2.pdf and http://www.arxiv-sanity.com/search?q=unsupervised (2) ImageNet supervision gives you much information to solve other computer vision tasks. So perhaps we…

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Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks — mentioned by nrmn

Wed, 23 Dec 2015 19:44:54 GMT

I've been trying to read a paper a day since midsummer. These are a few of the interesting, for me personally, since then: Generating Sequences With Recurrent Neural Networks - http://arxiv.org/abs/1308.0850 Older one, but important to understand deeply since other recent ideas have come from this! Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks - http://arxiv.org/abs/1511.06434 Unitary Evolution Recurrent Neural Networks - http://arxiv.org/abs/1511.06464 State of the Art Control of Atari Games Using Shallow Reinforcement Learning…

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Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks — mentioned by temuze

Wed, 23 Dec 2015 19:05:12 GMT

One weird trick for parallelizing ConvNets: http://arxiv.org/abs/1404.5997 Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks: http://arxiv.org/abs/1511.06434 A Neural Network of Artistic Style: http://arxiv.org/abs/1508.06576

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Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks — HN submission

Tue, 24 Nov 2015 02:02:19 GMT

Unsupervised Rep. Learning with Deep Convolutional Generative Advers. Nets

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