In this paper, we introduced the novel concept of advisor network to address
the problem of noisy labels in image classification. Deep neural networks (DNN)
are prone to performance reduction and overfitting problems on training data
with noisy annotations. Weighting loss methods aim to mitigate the influence of
noisy labels during the training, completely removing their contribution. This
discarding process prevents DNNs from learning wrong associations between
images and their correct labels but reduces the amount of data used, especially
when most of the samples have noisy labels. Differently, our method weighs the
feature extracted directly from the classifier without altering the loss value
of each data. The advisor helps to focus only on some part of the information
present in mislabeled examples, allowing the classifier to leverage that data
as well. We trained it with a meta-learning strategy so that it can adapt
throughout the training of the main model. We tested our method on CIFAR10 and
CIFAR100 with synthetic noise, and on Clothing1M which contains real-world
noise, reporting state-of-the-art results.

本文提出了基于顾问网络的新概念，旨在解决图像分类中存在噪声标签的问题。通过元学习方法训练顾问网络，不仅可以提高分类器的性能，还可以利用含有噪声标签的数据。该方法在 CIFAR10、CIFAR100 和 Clothing1M 数据集上的实验结果显示出了最先进的效果。