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Dank Learning: Generating Memes Using Deep Neural Networks

172 pointsby nevatiaritikaalmost 7 years ago

13 comments

minimaxiralmost 7 years ago
As someone who has spent a <i>lot</i> of time working with text-generating neural networks (<a href="https:&#x2F;&#x2F;github.com&#x2F;minimaxir&#x2F;textgenrnn" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;minimaxir&#x2F;textgenrnn</a>), I have a few quick comments.<p>1) The input dataset from Memegenerator is a bit weird. More importantly, <i>it does not distinctly identify top and bottom texts</i> (some have a capital letter to signifify the start of the bottom text, which isn&#x27;t always true). A good technique when encoding text for these types of things is to use a control token (e.g. a newline) to indicate these types of behaviors. (the conclusion notes this problem: &quot;One example would be to train on a dataset that includes the break point in the text between upper and lower for the image. These were chosen manually here and are important for the humor impact of the meme.&quot;)<p>2) The use of GLoVe embeddings don&#x27;t make as much sense here, even as a base. Generally the embeddings work best on text which follows real-world word usage, which memes do not follow. (in this case, it&#x27;s better to let the network train the embeddings from scratch)<p>3) A 512-cell LSTM might be too big for a word-level model of that size; since the text follows rules, a 256-cell Bidirectional might work better.
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glupalmost 7 years ago
Very silly; best not to alert the media or we&#x27;ll soon see &quot;AI can now generate memes&quot; clickbait.<p>I thought it was funny though that Richard Socher, one of the authors of GLoVe and NLP researcher is pictured in the generated memes on p. 8. (&quot;the face you make when&quot;)
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aw3c2almost 7 years ago
This is a complete joke, right? What is better about those results than a simple &quot;image + headline + random bottom line&quot; algorithm?
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nofinatoralmost 7 years ago
Full paper with some examples here: <a href="https:&#x2F;&#x2F;web.stanford.edu&#x2F;class&#x2F;cs224n&#x2F;reports&#x2F;6909159.pdf" rel="nofollow">https:&#x2F;&#x2F;web.stanford.edu&#x2F;class&#x2F;cs224n&#x2F;reports&#x2F;6909159.pdf</a>
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Xyzodiacalmost 7 years ago
I was expecting this to use some formats that aren&#x27;t from 2012. It would be interesting to see a neural network that could decide text for more complex meme formats that trend on twitter and instagram.
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Cthulhu_almost 7 years ago
Reminds me a bit of <a href="https:&#x2F;&#x2F;www.reddit.com&#x2F;r&#x2F;SubredditSimulator&#x2F;" rel="nofollow">https:&#x2F;&#x2F;www.reddit.com&#x2F;r&#x2F;SubredditSimulator&#x2F;</a>
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jcfreialmost 7 years ago
It looks like a joke now but I&#x27;m fairly convinced that in the not too distant future the most influential social media accounts will be run by some kind of AI.
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momaniaalmost 7 years ago
Let me leave this here: <a href="https:&#x2F;&#x2F;imgur.com&#x2F;a&#x2F;ZOcKWmp" rel="nofollow">https:&#x2F;&#x2F;imgur.com&#x2F;a&#x2F;ZOcKWmp</a>
Miltnoidalmost 7 years ago
Holy shit this has the NIPS format.<p>If this was submitted we are certainly in the dankest timeline.
typonalmost 7 years ago
All their generated examples look like Markov chain generated captions. Pretty random and generally unfunny. I completely disagree with the claim that you can&#x27;t differentiate between these generated memes and real memes. None of these would make the front page of reddit, for example.
mr__yalmost 7 years ago
that&#x27;s still funnier than 9gag
ferongralmost 7 years ago
They&#x27;re called image macros, not memes.
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a_r_8almost 7 years ago
Examples?
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