  Precisely-labeled data sets with sufficient amount of samples are very
important for training deep convolutional neural networks (CNNs). However, many
of the available real-world data sets contain erroneously labeled samples and
those errors substantially hinder the learning of very accurate CNN models. In
this work, we consider the problem of training a deep CNN model for image
classification with mislabeled training samples - an issue that is common in
real image data sets with tags supplied by amateur users. To solve this
problem, we propose an auxiliary image regularization technique, optimized by
the stochastic Alternating Direction Method of Multipliers (ADMM) algorithm,
that automatically exploits the mutual context information among training
images and encourages the model to select reliable images to robustify the
learning process. Comprehensive experiments on benchmark data sets clearly
demonstrate our proposed regularized CNN model is resistant to label noise in
training data.
