7 citations · 14 across the 6 of their papers we have counts for
6 papers
Learning with Noisy Ground Truth: From 2D Classification to 3D Reconstruction
Yangdi Lu, Wenbo He
Deep neural networks has been highly successful in data-intense computer vision applications, while such success relies heavily on the massive and clean data. In real-world scenari…
Mitigating Noisy Supervision Using Synthetic Samples with Soft Labels
Yangdi Lu, Wenbo He
Noisy labels are ubiquitous in real-world datasets, especially in the large-scale ones derived from crowdsourcing and web searching. It is challenging to train deep neural networks…
SELC: Self-Ensemble Label Correction Improves Learning with Noisy Labels
Yangdi Lu, Wenbo He
Deep neural networks are prone to overfitting noisy labels, resulting in poor generalization performance. To overcome this problem, we present a simple and effective method self-en…
Confidence Adaptive Regularization for Deep Learning with Noisy Labels
Yangdi Lu, Yang Bo, Wenbo He
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 mis…
Co-matching: Combating Noisy Labels by Augmentation Anchoring
Yangdi Lu, Yang Bo, Wenbo He
Deep learning with noisy labels is challenging as deep neural networks have the high capacity to memorize the noisy labels. In this paper, we propose a learning algorithm called Co…
CLTA: Contents and Length-based Temporal Attention for Few-shot Action Recognition
Yang Bo, Yangdi Lu, Wenbo He
Few-shot action recognition has attracted increasing attention due to the difficulty in acquiring the properly labelled training samples. Current works have shown that preserving s…