Title: IS THE LABEL TRUSTFUL: TRAINING BETTER DEEP LEARNING MODEL VIA UNCERTAINTY MINING NET. Abstract: In this work, we consider a new problem of training deep neural network on partially labeled data with label noise.  As far as we know, 
there have been very few efforts to tackle such problems.
We present a novel end-to-end deep generative pipeline for improving classifier performance when dealing with such data problems.  We call it 
Uncertainty Mining Net (UMN).  
 During the training stage, we utilize all the available data (labeled and unlabeled) to train the classifier via a semi-supervised generative framework. 
 During training, UMN estimates the uncertainly of the labels’ to focus on clean data for  learning. More precisely, UMN applies the sample-wise label uncertainty estimation scheme. 
 Extensive experiments and comparisons against state-of-the-art methods on several popular benchmark datasets demonstrate that UMN can reduce the effects of label noise and significantly improve classifier performance.