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Background Literature Review Latent Class-Conditional Noise Model Experiments Conclusion Safeguarded Dynamic Label Regression for Noisy Supervision Jiangchao Yao , , Hao Wu , Ya Zhang Ivor W. Tsang , Jun Sun

  1. Background Literature Review Latent Class-Conditional Noise Model Experiments Conclusion Safeguarded Dynamic Label Regression for Noisy Supervision Jiangchao Yao † , ‡ , Hao Wu † , Ya Zhang † Ivor W. Tsang ‡ , Jun Sun † † Shanghai Jiao Tong University ‡ University of Technology Sydney November 14, 2018 Safeguarded Dynamic Label Regression for Noisy Supervision 1/28

  2. Background Literature Review Latent Class-Conditional Noise Model Experiments Conclusion Outline 1 Background 2 Literature Review 3 Latent Class-Conditional Noise Model 4 Experiments 5 Conclusion Safeguarded Dynamic Label Regression for Noisy Supervision 2/28

  3. Background Literature Review Latent Class-Conditional Noise Model Experiments Conclusion Background Low Expensive Noisy Data Meets Deep Learning Inexhaustible social images with annotations on websites. Fine-grained annotations from crowdsourcing platforms. Rich medical diagnosis by numerous levels of doctors. Large amount of unreliable stock labels for revenue. Learning with Noisy Supervision Brings Robustness Existing deep learning based methods, Learning with Noise Transition Learning with Sample Re-weighting Learning with Model Regularization Safeguarded Dynamic Label Regression for Noisy Supervision 3/28

  4. Background Literature Review Latent Class-Conditional Noise Model Experiments Conclusion Outline 1 Background 2 Literature Review 3 Latent Class-Conditional Noise Model 4 Experiments 5 Conclusion Safeguarded Dynamic Label Regression for Noisy Supervision 4/28

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Literature Review Latent Class-Conditional Noise Model Experiments Conclusion Probabilistic Modeling Learning with Noise Transition x z y Learning with Noise Transition � ln P ( y | x ) = ln P ( y | z , x ) P ( z | x ) (1) � �� � � �� � z Noise transition Classifier Classification Risk: E x , z [ − ln P ( z | x )] � = E x , y [ − ln P ( y | x )] if z �≡ y . Safeguarded Dynamic Label Regression for Noisy Supervision 5/28

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