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Robust Temporal Ensembling for Learning with Noisy Labels
Successful training of deep neural networks with noisy labels is an essential capability as most real-world datasets contain some amount of mislabeled data.
Ranked #1 on
Image Classification
on mini WebVision 1.0
(ImageNet Top-5 Accuracy metric)
Robust Temporal Ensembling
In particular, RTE achieves 93. 64% accuracy on CIFAR-10 and 66. 43% accuracy on CIFAR-100 under 80% label corruption, and achieves 76. 72% accuracy on ImageNet under 40% corruption.
Accurate Trajectory Prediction for Autonomous Vehicles
Predicting vehicle trajectories, angle and speed is important for safe and comfortable driving.
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