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Will 2020 see more pretrained representation and transfer works? This simple (but large scale) BiT approach is quite effective on a wide number of datasets. You can see here *all* top-1 errors on CIFAR10 test. VTAB testbed is improved but still challenging http://arxiv.org/abs/1910.04867 https://twitter.com/giffmana/status/1214240746095730688 …pic.twitter.com/hnqZmwuIbe
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An update of our paper investigating object compositionality in GANs is now available: https://arxiv.org/abs/1810.10340 We show how a structured generator that learns about objects can facilitate unsupervised instance segmentation. w/
@karol_kurach@SchmidhuberAI@sylvain_gelly 1/4pic.twitter.com/S5qlVQ8jZx
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Using our Semantic Bottleneck GAN (SB-GAN), achieve SOTA results in synthesizing complex scenes from scratch as well as from real semantic layouts. Joint work with
@mtschannen,Eric Tzeng,@sylvain_gelly, Trevor Darrell,@MarioLucic_ https://arxiv.org/pdf/1911.11357.pdf … https://github.com/azadis/SB-GAN pic.twitter.com/0GM5HHedZH
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In our recent collaboration with
@berkeley_ai we show how to generate realistic complex scenes from scratch! While the problem is extremely challenging, we show how to achieve SOTA in unconditional generation and improve conditional generation using SPADE http://arxiv.org/pdf/1911.11357.pdf …pic.twitter.com/fVXkEvMdkt
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We are happy to announce the v2.0 release of the Google Research Football Environment. The most exciting feature of this release is the Game Server, which lets your agent compete online with other researchers' models. Visit https://research-football.dev and give it a try!https://twitter.com/GoogleAI/status/1137058715129925632 …
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We’re pleased to release the Visual Task Adaptation Benchmark (VTAB), a diverse, realistic, and challenging protocol to measure progress towards universal visual representations. Learn all about it below.https://goo.gle/2Noutb9
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Are you interested in Representation Learning, Transfer Learning, Domain Adaptation, Self-Supervised Learning or Semi-Supervised Learning? Have a look at this work from Google Brain Zurich!https://twitter.com/neilhoulsby/status/1184783549053968385 …
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Excited about recent progress in self-supervised representation learning based on mutual information maximisation? Mutual Information might not be the key ingredient for the success of these methods, as shown in our latest paper: http://arxiv.org/abs/1907.13625 pic.twitter.com/OTHl0UYFkd
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Code released to adapt BERT using few parameters. Can be used to adapt one model to many tasks. Catastrophic forgetting not included.https://github.com/google-research/adapter-bert …
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Congratulations to the Google,
@ETH and@MPI_IS authors of "Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations" (http://goo.gle/2IyFqTO ), recipient of an#ICML2019 Best Paper Award! Learn more in the blog post at http://goo.gle/2KaMs48 .Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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Check out a novel
#ReinforcementLearning environment where agents aim to master the world’s most popular sport—football! The Google Research Football Environment includes benchmarks & progressive RL training scenarios, and is available in open source beta→http://goo.gle/2Mz8IqG pic.twitter.com/sTx6oWc3BrPrikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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Quantitative evaluation of generative models is a key research challenge. In our latest work, we introduce a theoretical framework which uncovers the existing approaches based on precision and recall as special cases, and offers novel geometric insights. https://arxiv.org/abs/1905.10768 pic.twitter.com/jXDCYgeXDX
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Want to turn your self-supervised method into a semi-supervised learning technique? Check out our S⁴L framework (https://arxiv.org/abs/1905.03670 )! Work done at
@GoogleAI with@avitaloliver,@__kolesnikov__ and@giffmana.pic.twitter.com/Znx615RDeV
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Our work on "High-Fidelity Image Generation With Fewer Labels" has been accepted to ICML'19! Thanks to the reviewers and the area chairs for the thorough reviews!
@icmlconf@mtschannen@TheMarvinRitter@XiaohuaZhai@OlivierBachem@sylvain_gelly@GoogleAIpic.twitter.com/ifBMB0YxrUHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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Learning disentangled representations of a scene is critical for many machine vision tasks. In collaboration with
@ETH and@MPI_IS, we present a broad examination of the field, examine the role of implicit biases and provide direction for future research.https://goo.gl/Tp3x2cHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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Check out some research exploring a new approach to training conditional generative adversarial networks (GANs) that reduces the amount of labeled data required by a factor of ~10 (along with an update to the Compare GAN library!). Learn more at ↓https://goo.gl/yMau1n
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A year ago successfully training GANs on ImageNet without labels seemed out of reach. Now, we can obtain samples such as the ones below. It's amazing what increased compute + new insights/techniques can achieve. https://arxiv.org/abs/1903.02271
@GoogleAI@ETH_enpic.twitter.com/2aVrYuFkfkHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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Self-supervision + clustering + BigGAN = FID 22.0 for image synthesis on ImageNet without labels. Check out all the samples in our full paper at https://goo.gl/idWNVs .
@GoogleAI@ETH_en@MarioLucic_@TheMarvinRitter@mtschannen@sylvain_gelly@XiaohuaZhaipic.twitter.com/6n9uaEUHEH
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How to train SOTA high-fidelity conditional GANs usin 10x fewer labels? Using self-supervision and semi-supervision! Check out our latest work at https://goo.gl/idWNVs
@GoogleAI@ETHZurich@TheMarvinRitter@mtschannen@XiaohuaZhai@OlivierBachem@sylvain_gellyHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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In collaboration with
@DeepMindAI and@ETH, we have open sourced the code for the ICLR'19 paper "Episodic Curiosity through Reachability". Check out the paper (including new locomotion experiments!) at http://goo.gl/b5d6so and the code at http://goo.gl/dNW7YE .https://twitter.com/GoogleAI/status/1055142761354022912 …
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