Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey <s> C. Multiple-Instance Learning <s> In this work, we formulate a new weakly supervised domain generalization approach for visual recognition by using loosely labeled web images/videos as training data. Specifically, we aim to address two challenging issues when learning robust classifiers: 1) coping with noise in the labels of training web images/videos in the source domain; and 2) enhancing generalization capability of learnt classifiers to any unseen target domain. To address the first issue, we partition the training samples in each class into multiple clusters. By treating each cluster as a “bag” and the samples in each cluster as “instances”, we formulate a multi-instance learning (MIL) problem by selecting a subset of training samples from each training bag and simultaneously learning the optimal classifiers based on the selected samples. To address the second issue, we assume the training web images/videos may come from multiple hidden domains with different data distributions. We then extend our MIL formulation to learn one classifier for each class and each latent domain such that multiple classifiers from each class can be effectively integrated to achieve better generalization capability. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our new approach for visual recognition by learning from web data. <s> BIB001 </s> Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey <s> C. Multiple-Instance Learning <s> Large-scale datasets have driven the rapid development of deep neural networks for visual recognition. However, annotating a massive dataset is expensive and time-consuming. Web images and their labels are, in comparison, much easier to obtain, but direct training on such automatially harvested images can lead to unsatisfactory performance, because the noisy labels of Web images adversely affect the learned recognition models. To address this drawback we propose an end-to-end weakly-supervised deep learning framework which is robust to the label noise in Web images. The proposed framework relies on two unified strategies – random grouping and attention – to effectively reduce the negative impact of noisy web image annotations. Specifically, random grouping stacks multiple images into a single training instance and thus increases the labeling accuracy at the instance level. Attention, on the other hand, suppresses the noisy signals from both incorrectly labeled images and less discriminative image regions. By conducting intensive experiments on two challenging datasets, including a newly collected fine-grained dataset with Web images of different car models, the superior performance of the proposed methods over competitive baselines is clearly demonstrated. <s> BIB002
In multiple-instance learning (MIL), data is grouped in clusters, called as bags, and each bag is labeled as positive if there is at least one positive instance in it and negative otherwise. Network is fed with group of data and produces a single prediction for each bag by learning inner discriminative representation of data. Since group of images are used and one prediction is produced, existence of noisy labels along with true labels in a bag has less impact on learning. In BIB001 authors propose to effectively choose training samples from each bag by minimizing the total bag level loss to train noise robust classifiers. Extra model is trained in BIB002 as attention model, which chooses parts of the images to be focused on. Aim is to focus on few regions on correctly labeled image and not focus on any region for mislabeled images.