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This is a complete joke, right? What is better about those results than a simple "image + headline + random bottom line" algorithm?
by aw3c2 8y ago
This is a complete joke, right? What is better about those results than a simple "image + headline + random bottom line" algorithm?
- ekianjo 8y agoExactly. Memes are funny because they make meta references that are culturally relevant or simply attach absurd bottom lines. It's highly unlikely a deep neural network can model anything like that.
- stochastic_monk 8y agoI think the image needs to be an input somehow. I imagine running an image classifier (e.g., YOLO9000) to extract “pretrained” features and making those values inputs into a modified LSTM could allow learning to synthesize text and perception. I’d suggest learning new image embeddings (training a neural network to extract image features from scratch), but it’d be difficult to get enough images/enough different images.
- dmschulman 8y agoConsidering most deep learning results are interpreted as absurd/bizarre, I don't think the machine will have much difficulty intentionally or unintentionally emulating meme culture.
- vertexFarm 8y agoThat was my thought. They need to crank the noise way up and aim for some surreal memes, not these ancient fossilized memes from 2010.
- wodenokoto 8y agoJudging from the url posted in an earlier top thread, this might be a student report. https://web.stanford.edu/class/cs224n/reports/6909159.pdf https://web.stanford.edu/class/cs224n/reports/6909159.pdf
- camelCaseOfBeer 8y agoI'd put $100 on the researcher coming up with the title and working from there. "Dank Learning"? Come on, it's a meme in itself. That said, worth publishing? Sure it's at the top of HN. Ground breaking results, nah. Though, I admit I am impressed with the applied solution, using deep learning and some apriori direction to derive context from images is neat.