Meta-learning Advisor Networks for Long-tail and Noisy Labels in Social Image Classification

Abstract

Deep neural networks (DNNs) for social image classification are prone to performance reduction and overfitting when trained on datasets plagued by noisy or imbalanced labels. Weight loss methods tend to ignore the influence of noisy or frequent category examples during the training, resulting in a reduction of final accuracy and, in the presence of extreme noise, even a failure of the learning process. A new advisor network is introduced to address both imbalance and noise problems, and is able to pilot learning of a main network by adjusting the visual features and the gradient with a meta-learning strategy. In a curriculum learning fashion, the impact of redundant data is reduced while recognizable noisy label images are downplayed or redirected. Meta Feature Re-Weighting (MFRW) and Meta Equalization Softmax (MES) methods are introduced to let the main network focus only on the information in an image deemed relevant by the advisor network and to adjust the training gradient to reduce the adverse effects of frequent or noisy categories. The proposed method is first tested on synthetic versions of CIFAR10 and CIFAR100, and then on the more realistic ImageNet-LT, Places-LT, and Clothing1M datasets, reporting state-of-the-art results.