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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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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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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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Hi Andrej, here you go (Figure 8 in arxiv paper: https://arxiv.org/pdf/1905.11946.pdf …).pic.twitter.com/YKzTnG3f5Z
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