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Achieving Robustness in the Wild via Adversarial Mixing with Disentangled Representations

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DeepMind: Training data is often collected through a biased process. Models trained on such data are inherently biased. We demonstrate how adversarial training through disentangled representations can reduce the effect of spurious correlations present in datasets: http://arxiv.org/abs/1912.03192 https://t.co/YjJpnY1E8k

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roadrunner01: Achieving Robustness in the Wild via Adversarial Mixing with Disentangled Representations pdf: https://arxiv.org/pdf/1912.03192.pdf abs: https://arxiv.org/abs/1912.03192 https://t.co/Mk7vKyNSgF

0 replies, 37 likes


pushmeet: Excited to share recent work from our team @DeepMindAI that tries to make sure that ML systems generalize properly to specific variations encountered in the real world.

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HubReports | HubBucket HealthIT Algorithm Auditing: #MachineLearning and #DeepLearning #Algorithm Training #Data is often collected through a #Biased process. 🥇@DeepMindAI shows how Adversarial Training through Disentangled Representations can REDUCE the #Race and #Gender #Bias in #Datasets 🖥️http://arxiv.org/abs/1912.03192 https://t.co/uVC3qajfOm

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Sven Gowal: Our latest work demonstrates that it is possible to reduce the effect of bias using adversarial mixing on top of disentangled representations (as provided by StyleGAN models for example).

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Content

Found on Dec 12 2019 at https://arxiv.org/pdf/1912.03192.pdf

PDF content of a computer science paper: Achieving Robustness in the Wild via Adversarial Mixing with Disentangled Representations