Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey

Image classification systems recently made a giant leap with the advancement of deep neural networks. However, these systems require an excessive amount of labeled data to be adequately trained. Gathering a correctly annotated dataset is not always feasible due to several factors, such as the expensiveness of the labeling process or difficulty of correctly classifying data, even for the experts. Because of these practical challenges, label noise is a common problem in real-world datasets, and numerous methods to train deep neural networks with label noise are proposed in the literature. Although deep neural networks are known to be relatively robust to label noise, their tendency to overfit data makes them vulnerable to memorizing even random noise. Therefore, it is crucial to consider the existence of label noise and develop counter algorithms to fade away its adverse effects to train deep neural networks efficiently. Even though an extensive survey of machine learning techniques under label noise exists, the literature lacks a comprehensive survey of methodologies centered explicitly around deep learning in the presence of noisy labels. This paper aims to present these algorithms while categorizing them into one of the two subgroups: noise model based and noise model free methods. Algorithms in the first group aim to estimate the noise structure and use this information to avoid the adverse effects of noisy labels. Differently, methods in the second group try to come up with inherently noise robust algorithms by using approaches like robust losses, regularizers or other learning paradigms

Multi-Label Noise Robust Collaborative Learning Model for Remote Sensing Image Classification

The development of accurate methods for multi-label classification (MLC) of remote sensing (RS) images is one of the most important research topics in RS. Methods based on Deep Convolutional Neural Networks (CNNs) have shown strong performance gains in RS MLC problems. However, CNN-based methods usually require a high number of reliable training images annotated by multiple land-cover class labels. Collecting such data is time-consuming and costly. To address this problem, the publicly available thematic products, which can include noisy labels, can be used to annotate RS images with zero-labeling cost. However, multi-label noise (which can be associated with wrong and missing label annotations) can distort the learning process of the MLC algorithm. The detection and correction of label noise are challenging tasks, especially in a multi-label scenario, where each image can be associated with more than one label. To address this problem, we propose a novel noise robust collaborative multi-label learning (RCML) method to alleviate the adverse effects of multi-label noise during the training phase of the CNN model. RCML identifies, ranks and excludes noisy multi-labels in RS images based on three main modules: 1) discrepancy module; 2) group lasso module; and 3) swap module. The discrepancy module ensures that the two networks learn diverse features, while producing the same predictions. The task of the group lasso module is to detect the potentially noisy labels assigned to the multi-labeled training images, while the swap module task is devoted to exchanging the ranking information between two networks. Unlike existing methods that make assumptions about the noise distribution, our proposed RCML does not make any prior assumption about the type of noise in the training set. Our code is publicly available online: http://www.noisy-labels-in-rs.orgComment: Our code is publicly available online: http://www.noisy-labels-in-rs.or