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Confidence Adaptive Regularization for Deep Learning with Noisy Labels
Recent studies on the memorization effects of deep neural networks on noisy labels show that the networks first fit the correctly-labeled training samples before memorizing the mislabeled samples.
Ranked #28 on
Image Classification
on mini WebVision 1.0
Co-matching: Combating Noisy Labels by Augmentation Anchoring
We then update two networks simultaneously by selecting small-loss instances to minimize both unsupervised matching loss (i. e., measure the consistency of the two networks) and supervised classification loss (i. e. measure the classification performance).
CLTA: Contents and Length-based Temporal Attention for Few-shot Action Recognition
Few-shot action recognition has attracted increasing attention due to the difficulty in acquiring the properly labelled training samples.
