RML++: Regroup Median Loss for Combating Label Noise

Keyphrases

• Training Data 100%
• Label Noise 100%
• Data Sampling 100%
• Loss Correction 100%
• Training Samples 50%
• State-of-the-art Techniques 50%
• Negative Impact 50%
• Sampling Methods 50%
• Source Code 50%
• Separation Approach 50%
• Model Prediction 50%
• Biased Sampling 50%
• Annotated Dataset 50%
• Training Deep Neural Networks 50%
• Number of Training Samples 50%
• Noisy Samples 50%
• Noise Rate 50%
• Noisy Training 50%
• Adaptive Threshold 50%
• Prediction Consistency 50%

Computer Science

• Training Sample 100%
• Sampling Data 100%
• Training Dataset 100%
• Regularization 50%
• Negative Impact 50%
• Deep Neural Network 50%
• Model Prediction 50%
• Priori Information 50%

Earth and Planetary Sciences

• Data Sampling 100%
• State of the Art 50%