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Adversarial Latent Autoencoders

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hardmaru: Adversarial Latent Autoencoders Kind of creepy to imagine what my entire family might look like as Tyrion Lannister, Neo, or Emma Watson. https://arxiv.org/abs/2004.04467 https://github.com/podgorskiy/ALAE https://t.co/0DLQcNkdu0

6 replies, 663 likes


Stanislav Pidhorskyi: Checkout "Adversarial Latent Autoencoders" (ALAE), #CVPR2020 Preprint: https://arxiv.org/abs/2004.04467 Code and pre-trained models: https://github.com/podgorskiy/ALAE #CVPR, #AI, #ML https://t.co/pt0CJZAUOf

2 replies, 89 likes


Shirley Ho: One can imagine lots of physical applications of this adversarial latent autoencoder. But it would be interesting to morph our politicians into #GameofThrones characters ... who should @realDonaldTrump be?

2 replies, 44 likes


Daisuke Okanohara: ALAE is the first autoencoder that can generate high fidelity images as ones by Style-GAN. ALAE uses the combination of the adversarial loss in image space and the reconstruction error in the "learned" latent space. GAN quality with encoding capability. https://arxiv.org/abs/2004.04467

1 replies, 37 likes


AverageName: To start with, I'll try to analyze this paper: [CVPR2020] Adversarial Latent Autoencoders https://arxiv.org/pdf/2004.04467.pdf The main idea is to combine ideas of GANs and VAEs, by telling that our discriminator and generator should have shared latent space.

1 replies, 7 likes


Alberto Tono: ๐Ÿ‘‰๐Ÿป๐Ÿ’ซGreat weekend reading. ๐ŸŒˆStyleALAE: https://arxiv.org/abs/2004.04467 you can learn the probability distribution of the latent space (with data distribution in adversarial settings). It enables learning representations that are likely less entangled. #ai #ml #DeepLearning ๐Ÿ’ซ

0 replies, 3 likes


Vladimir: ๐—”๐—ฑ๐˜ƒ๐—ฒ๐—ฟ๐˜€๐—ฎ๐—ฟ๐—ถ๐—ฎ๐—น ๐—Ÿ๐—ฎ๐˜๐—ฒ๐—ป๐˜ ๐—”๐˜‚๐˜๐—ผ๐—ฒ๐—ป๐—ฐ๐—ผ๐—ฑ๐—ฒ๐—ฟ๐˜€ ๐Ÿค–. Wow, how much the output quality has increased in the last two years, deepfakes are real ๐Ÿ˜ฑ๐Ÿ˜ฑ๐Ÿ˜ฑ Paper https://arxiv.org/pdf/2004.04467.pdf GitHub https://github.com/podgorskiy/ALAE #MachineLearning #ArtificialIntelligence https://t.co/1uQTypmhBx

1 replies, 2 likes


akira: https://arxiv.org/abs/2004.04467 By splitting the Encoder and Decoder into two parts and letting them learn the distribution of intermediate representations adversely like GAN, authors propose an AutoEncoder that can achieve both Sota GAN level expressiveness and an organized latent space https://t.co/N7Uw2MC1V7

0 replies, 1 likes


Content

Found on Apr 22 2020 at https://arxiv.org/pdf/2004.04467.pdf

PDF content of a computer science paper: Adversarial Latent Autoencoders