Google Inc.

Deepmind Technologies Limited

Rudy Bunel

Ian Goodfellow

Sven Adrian Gowal

Olaf Ronneberger

Seyed Mohammadali Eslami

Christopher Gamble

Jim Huibrecht Winkens

Simon Kohl

Ali Taylan Cemgil

Abhijit Guha Roy

Sree Meghana Thotakuri

Sylvestre-Alvise Guglielmo Rebuffi

Florian Nils Stimberg

Miklós Zsigmond Horváth

Olivia Anne Wiles

Jamie Hayes

David Stutz

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a neural network to (i) generate accurate network outputs for a machine learning task and (ii) generate intermediate outputs that can be used to reliably classify out-of-distribution inputs. In one aspect, a method comprises: training the neural network using supervised and contrastive losses, comprising repeatedly performing operations including: obtaining first and second network inputs; processing each network input using the neural network to generate its respective network input embedding; processing the first network input using the neural network to generate a network output; and adjusting the network parameter values using supervised and contrastive loss gradients, wherein: the supervised loss is based on: (i) the network output, and (ii) a corresponding target network output; and the contrastive loss is based on at least: (i) the first network input embedding, and (ii) the second network input embedding.

Classifying out-of-distribution data using a contrastive loss

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for verifying the provenance of a digital object generated by a neural network, such as an image or audio object. Also methods, systems, and apparatus, including computer programs, for training a watermarking neural network and a watermark decoding neural network. The described techniques make efficient use of computing resources and are robust to attack.

Verifying the provenance of a digital object using watermarking and embeddings

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London, United Kingdom

Rudy Bunel