olivierhenaff

@olivierhenaff

Research Scientist at DeepMind, interested in how brains and machines can learn without supervision

London, UK
Vrijeme pridruživanja: svibanj 2019.
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  1. Prikvačeni tweet
    9. pro 2019.

    Very happy to share our latest unsupervised representation learning work! In addition to SOTA linear classification, we beat supervised networks on ImageNet with 2-5x less labels and transfer to PASCAL detection better than supervised pre-training.

    1 reply 23 proslijeđena tweeta 110 korisnika označava da im se sviđa
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  2. proslijedio/la je Tweet
    9. pro 2019.

    Exciting updated results for self-supervised representation learning on ImageNet: - 71.5% top-1 with a *linear* classifier - 77.9% top-5 with only *1%* of the labels - 76.6 mAP when transferred to PASCAL VOC-07 (better than *fully-supervised's* 74.7 mAP)

    1 reply 12 proslijeđenih tweetova 74 korisnika označavaju da im se sviđa
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  3. proslijedio/la je Tweet
    9. pro 2019.

    Beating previous state of the art in self-supervised learning for ImageNet by almost 3% absolute with less parameters (71.5% vs 68.6% top1). Extensive results for data-efficient learning on both ImageNet and Pascal VOC in the updated

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    5 proslijeđenih tweetova 14 korisnika označava da im se sviđa
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  4. proslijedio/la je Tweet
    9. pro 2019.

    Unsupervised pre-training now outperforms supervised learning on ImageNet for any data regime (see figure) and also for transfer learning to Pascal VOC object detection

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    218 proslijeđenih tweetova 778 korisnika označava da im se sviđa
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  5. 9. pro 2019.

    with

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  6. proslijedio/la je Tweet
    23. svi 2019.

    Deep learning has so far relied on massive amounts of supervision. We show that unsupervised representation learning with Contrastive Predictive Coding greatly improves data-efficiency: By and

    6 replies 270 proslijeđenih tweetova 797 korisnika označava da im se sviđa
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