  There is an emerging trend to leverage noisy image datasets in many visual
recognition tasks. However, the label noise among the datasets severely
degenerates the \mbox{performance of deep} learning approaches. Recently, one
mainstream is to introduce the latent label to handle label noise, which has
shown promising improvement in the network designs. Nevertheless, the mismatch
between latent labels and noisy labels still affects the predictions in such
methods. To address this issue, we propose a quality embedding model, which
explicitly introduces a quality variable to represent the trustworthiness of
noisy labels. Our key idea is to identify the mismatch between the latent and
noisy labels by embedding the quality variables into different subspaces, which
effectively minimizes the noise effect. At the same time, the high-quality
labels is still able to be applied for training. To instantiate the model, we
further propose a Contrastive-Additive Noise network (CAN), which consists of
two important layers: (1) the contrastive layer estimates the quality variable
in the embedding space to reduce noise effect; and (2) the additive layer
aggregates the prior predictions and noisy labels as the posterior to train the
classifier. Moreover, to tackle the optimization difficulty, we deduce an SGD
algorithm with the reparameterization tricks, which makes our method scalable
to big data. We conduct the experimental evaluation of the proposed method over
a range of noisy image datasets. Comprehensive results have demonstrated CAN
outperforms the state-of-the-art deep learning approaches.
