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I am excited and honored that we received the
#ICML2019 Best Paper Award with "Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations" (http://proceedings.mlr.press/v97/locatello19a.html …). W/@francescolocat8, S. Bauer,@MarioLucic_,@grx,@sylvain_gelly,@bschoelkopfpic.twitter.com/sucMQqsP1M
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Olivier Bachem proslijedio/la je Tweet
We distill key components for pre-training representations at scale: BigTransfer ("BiT") achieves SOTA on many benchmarks with ResNet, e.g. 87.8% top-1 on ImageNet (86.4% with only 25 images/class) and 99.3% on CIFAR-10 (97.6% with only 10 images/class). http://arxiv.org/abs/1912.11370 pic.twitter.com/bQULupLQzi
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Olivier Bachem proslijedio/la je Tweet
How much supervision do you need to learn disentangled representations? Turns out, not that much! Joint work with
@mtschannen, S. Bauer,@gxr,@bschoelkopf and@OlivierBachem. Accepted at#ICLR2020@GoogleAI@ETH_en@MPI_IShttps://openreview.net/forum?id=SygagpEKwB …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Olivier Bachem proslijedio/la je Tweet
Our work which critically investigates the role of mutual information in self-supervised representation learning was accepted to
#ICLR2020. http://openreview.net/forum?id=rkxoh24FPH … w/@mtschannen J. Djolonga@PaulKRubenstein@sylvain_gelly@GoogleAIpic.twitter.com/PFexJS56vT
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Olivier Bachem proslijedio/la je Tweet
It was great collaborating with you Paul, happy to hear you had a great time!https://twitter.com/googlestudents/status/1205547509419925504 …
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Olivier Bachem proslijedio/la je Tweet
3) Are Disentangled Representations Helpful for Abstract Visual Reasoning? Poster 28. Joint work with
@vansteenkiste_s,@SchmidhuberAI, and@OlivierBachem If you want to talk to me, I'll be at poster 34! (3/3)Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Olivier Bachem proslijedio/la je Tweet
2) On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset: Poster 35. Joint work with Gondal, Bauer S.,
@bschoelkopf,@OlivierBachem et al. (2/3)Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Olivier Bachem proslijedio/la je Tweet
Big disentanglement line-up today 17:00 at
#NeurIPS2019! 1) On the Fairness of Disentangled Representations: Poster 34. Joint work with Abbati G.,@tom_rainforth, Bauer S.,@bschoelkopf and@OlivierBachem (1/3)Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Olivier Bachem proslijedio/la je Tweet
Are disentangled representations helpful for abstract visual reasoning? Come find out later today during the afternoon poster session at 17:00, poster board 28. This is joint work with
@FrancescoLocat8 ,@SchmidhuberAI , and@OlivierBachempic.twitter.com/qgUyZxmtek
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Olivier Bachem proslijedio/la je Tweet
Join
@karol_kurach, Piotr Stanczyk,@OlivierBachem,@rikelhood and@MMMichalski now at our#NeurIPS2019 booth to learn the latest about the Google Research Football Environment (http://research-football.dev/about ). Also, learn about Dataset Search (http://goo.gle/3688ITj ) with@chrisgorgo!Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Olivier Bachem proslijedio/la je Tweet
Check out Semantic Bottleneck GAN: 1) Generating semantic label maps from scratch and 2) using SPADE to translate them into realistic images yields SOTA unconditional generation of high-resolution complex scenes!
@berkeley_ai@GoogleAI https://arxiv.org/pdf/1911.11357.pdf …pic.twitter.com/2xhcIlCdsh
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Olivier Bachem proslijedio/la je Tweet
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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Check out v2.0 of Google Research Football which includes a game server where your agent can challenge other agents!https://twitter.com/GoogleAI/status/1199453765272604672 …
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Olivier Bachem proslijedio/la je Tweet
Interested in visual representation learning, but tired of ImageNet, Cifar, & VOC? Remote Sensing is a research area with many important applications. To dig deeper check out our paper with
@neu_maximhttps://twitter.com/neu_maxim/status/1197498534729265152 …
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I'm excited that disentanglement_lib now supports the Unsupervised Disentanglement Ranking (UDR) method thanks to a code contribution by
@sunnyyduan,@irinavlh and their co-authors. Code: https://github.com/google-research/disentanglement_lib#udr-experiments … Paper: https://arxiv.org/abs/1905.12614 pic.twitter.com/eK2TPwW5vm
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Olivier Bachem proslijedio/la je Tweet
On the Fairness of Disentangled Representations (poster Thu Dec 12th) by
@FrancescoLocat8, Gabriele Abbati,@tom_rainforth, Stefan Bauer,@bschoelkopf@OlivierBachem with@MPI_IS@GoogleAIPrikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Olivier Bachem proslijedio/la je Tweet
Joint work with fantastic collaborators Gabriele Abbati,
@tom_rainforth, Stefan Bauer,@bschoelkopf and@OlivierBachem from@CSatETH,@ETH_en,@MPI_IS,@UniofOxford and@GoogleAI.Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Olivier Bachem proslijedio/la je Tweet
Interested in Fairness and Representation Learning? The code to reproduce our
#NeurIPS paper “On the Fairness of Disentangled Representations” https://arxiv.org/pdf/1905.13662.pdf … is now available athttps://github.com/google-research/disentanglement_lib …Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Excited that our paper "Are Disentangled Representations Helpful for Abstract Visual Reasoning?" (https://arxiv.org/abs/1905.12506 ) is accepted to
#NeurIPS2019 with code released at https://github.com/google-research/disentanglement_lib#abstract-reasoning-experiments …. With@vansteenkiste_s,@FrancescoLocat8 &@SchmidhuberAI.pic.twitter.com/De5I82IPqb
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Olivier Bachem proslijedio/la je Tweet
I wrote up my experience training PPO on the GFootball environment, reproducing the paper's results, and some fun stuff to do with a trained model here:https://towardsdatascience.com/reproducing-google-research-football-rl-results-ac75cf17190e …
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Check out our recent benchmark for representation learning!https://twitter.com/neilhoulsby/status/1184783549053968385 …
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