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Mingxing Tan proslijedio/la je Tweet
What I did over my winter break! It gives me great pleasure to share this summary of some of our work in 2019, on behalf of all my colleagues at
@GoogleAI &@GoogleHealth.https://ai.googleblog.com/2020/01/google-research-looking-back-at-2019.html …Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
This video explains AdvProp. Thanks
@CShorten30 !https://twitter.com/CShorten30/status/1201891652954140672 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Mingxing Tan proslijedio/la je Tweet
The TLDR of the paper; use adversarial examples as training data augmentation, maintain separate BatchNorm for normal vs adversarial examples. Neat. As usual I've ported & tested
#PyTorch weightshttps://github.com/rwightman/gen-efficientnet-pytorch …Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Can adversarial examples improve image recognition? Check out our recent work: AdvProp, achieving ImageNet top-1 accuracy 85.5% (no extra data) with adversarial examples! Arxiv: https://arxiv.org/abs/1911.09665 Checkpoints: https://git.io/JeopW pic.twitter.com/bAu054LGt2
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Excited to share our work on efficient neural architectures for object detection! New state-of-the-art accuracy (51 mAP on COCO for single-model single-scale), with an order-of-magnitude better efficiency! Collaborated with
@quocleix and@ruomingpang.https://twitter.com/quocleix/status/1197682880173920256 …
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Mingxing Tan proslijedio/la je Tweet
Full comparison against state-of-the-art on ImageNet. Noisy Student is our method. Noisy Student + EfficientNet is 11% better than your favorite ResNet-50
pic.twitter.com/BhwgJvSOYK
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Mingxing Tan proslijedio/la je Tweet
Want to improve accuracy and robustness of your model? Use unlabeled data! Our new work uses self-training on unlabeled data to achieve 87.4% top-1 on ImageNet, 1% better than SOTA. Huge gains are seen on harder benchmarks (ImageNet-A, C and P). Link: https://arxiv.org/abs/1911.04252 pic.twitter.com/0umSnX7wui
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Mingxing Tan proslijedio/la je Tweet
Great to see this collaboration between Google researchers & engineers launch, with major improvement to search quality! The work brings together many things we've been working on over the last few years: Transformers, BERT,
@TensorFlow, TPU pods, ...https://www.blog.google/products/search/search-language-understanding-bert …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
AutoML for video neural architecture design. Results are quite promising!https://twitter.com/GoogleAI/status/1184920718913363968 …
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Introducing EfficientNet-EdgeTPU: customized for mobile accelerators, with higher accuracy and 10x faster inference speed. blog post: https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html … Code and pertained models: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/edgetpu … https://twitter.com/GoogleAI/status/1158804847488978944 …pic.twitter.com/Vbj6aRHQMi
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Mingxing Tan proslijedio/la je Tweet
We released all checkpoints and training recipes of EfficientNets, including the best model EfficientNet-B7 that achieves accuracy of 84.5% top-1 on ImageNet. Link: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet …pic.twitter.com/vT7UojqOc0
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Introducing MixNet: AutoML + a new mixed depthwise conv (MDConv). SOTA results for mobile: 78.9% ImageNet top-1 accuracy under typical mobile settings (<600M FLOPS). Paper: https://arxiv.org/abs/1907.09595 Code & models: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet …pic.twitter.com/qP3XbpQ7Zb
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AutoAugment works pretty well on detection as well.https://twitter.com/quocleix/status/1144095735161348096 …
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Mingxing Tan proslijedio/la je Tweet
New work by Mingxing Tan and
@quocleix of@GoogleAI on automatically designing much more efficient-and-highly-accurate computer vision models. This will enable more sophisticated uses of computer vision on mobile devices, et al. Graph below highlights cost v. accuracy tradeoff.https://twitter.com/quocleix/status/1133833673134862337 …
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Mingxing Tan proslijedio/la je Tweet
EfficientNets: a family of more efficient & accurate image classification models. Found by architecture search and scaled up by one weird trick. Link: https://arxiv.org/abs/1905.11946 Github: https://bit.ly/30UojnC Blog: https://bit.ly/2JKY3qt pic.twitter.com/RIwvhCBA8x
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EfficientNet: surpass state-of-the-art accuracy with 10x better efficiency! If you are still using ResNet or Inception, please give it a try: EfficientNets are up to 16x more efficient than ResNet, and up to 13x more efficient than Inception on ImageNet.https://twitter.com/GoogleAI/status/1133834100568018945 …
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Mingxing Tan proslijedio/la je Tweet
As a pro tip provided by the baker, it turns out you can cut tiramisu with dental floss.pic.twitter.com/UQJjOR5sZs
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Mingxing Tan proslijedio/la je Tweet
Introducing MobileNetV3: Based on MNASNet, found by architecture search, we applied additional methods to go even further (quantization friendly SqueezeExcite & Swish + NetAdapt + Compact layers). Result: 2x faster and more accurate than MobileNetV2. Link: https://arxiv.org/abs/1905.02244 pic.twitter.com/jEFBeA67sR
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Mingxing Tan proslijedio/la je Tweet
On behalf of the whole Google Research &
@GoogleAI community, I was excited to put together a post describing some of the work that we collectively did in 2018. I hope you enjoy it! Thanks to everyone who helped make this work possible!https://ai.googleblog.com/2019/01/looking-back-at-googles-research.html …Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Mingxing Tan proslijedio/la je Tweet
Inspired by recent progress in neural architecture search, Google researchers explore an automated approach for designing mobile
#MachineLearning models with both high accuracy and speed, with results that outperform current state-of-the-art mobile models.http://goo.gl/WtPLufHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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