These style-based generator results look great: https://t.co/RL825n0yNP pic.twitter.com/k7UtJMTWhM
— Ian Goodfellow (@goodfellow_ian) December 13, 2018
These style-based generator results look great: https://t.co/RL825n0yNP pic.twitter.com/k7UtJMTWhM
— Ian Goodfellow (@goodfellow_ian) December 13, 2018
I tried it on a YouTube video clip called “We followed a Waymo self-driving car for miles, here's what we saw” and here's what it generated: https://t.co/mX5AoEz248 pic.twitter.com/bR1043OSmr
— hardmaru (@hardmaru) December 12, 2018
Pre-trained network for image super resolution (in Keras): https://t.co/vIZdf1C4El
— François Chollet (@fchollet) December 10, 2018
An evening project would be to export it to TF.js to run in the browser on user-uploaded photos
This is a far better-than-average paper. Sensible, yet simple approach, with very usable code provided, and well-designed experiments. Transfer learning accuracy on Pascal increases from 70%->75% - not bad! https://t.co/RGaRtADrlh
— Jeremy Howard (@jeremyphoward) December 10, 2018
There are many many useful ideas here - if you're ready to go to the next level of your understanding of fastai and pytorch, this is a great resource. https://t.co/jknG7sWtjg
— Jeremy Howard (@jeremyphoward) December 8, 2018
IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis. They propose a clever way to combine VAE+GAN by reusing the encoder of the VAE as the discriminator, allowing VAE to generate hi-res images. Found this gem at #NeurIPS2018. https://t.co/WQl4DdLyyk pic.twitter.com/hrOpPsJOwT
— hardmaru (@hardmaru) December 4, 2018
Bilinear Attention Networks #NIPS2018
— ML Review (@ml_review) December 4, 2018
By @jnhwkim
Extend the idea of co-attention into bilinear attention which considers every pair of multimodal channels. Achieves VGA 2.0 and Flickr30k SoTAhttps://t.co/KUV7xx4sdR pic.twitter.com/FpKTONS3O8
Can you identify a whale by its tail? 🐳 Analyze #Happywhale’s database of 25K+ images to help scientists better understand this important marine mammal population. You may even win part of a $25,000 prize pool. Get started! https://t.co/6l16J55d7i pic.twitter.com/bmsr7UKBMX
— Kaggle (@kaggle) December 3, 2018
😢 Missed @opencpu's talk? @rOpenSci's got you covered…
— Mara Averick (@dataandme) November 29, 2018
📸 "Working w/ images in R" #rstats
🎬 vide: https://t.co/TYZ2YQxnVe
📽 slides: https://t.co/QZCLKMgmEM
📝 collaborative notes: https://t.co/QZCLKMgmEM pic.twitter.com/HBkYhWTv3s
Neural networks fooled by unusual poses https://t.co/NE51FX8rNt pic.twitter.com/6o7y4RDCSo
— Carl Vondrick (@cvondrick) November 29, 2018
CariGANs -- drawing caricatured faces, like those street artists around Times Square. goes in reverse too. very cute idea (╹◡╹)凸 https://t.co/iuNL6i1ujA pic.twitter.com/TtXutnJZ0z
— Gene Kogan (@genekogan) November 22, 2018
Beating Fine-Grained Clothing Classification benchmark using fastai in 10 Lines of Code by @pankajmathur_ https://t.co/lc3FuoKXRZ
— Jeremy Howard (@jeremyphoward) November 21, 2018