Abstract
Active learning aims to select the most valuable unlabelled samples for annotation. In this paper, we propose a redundancy removal adversarial active learning (RRAAL) method based on norm online uncertainty indicator, which selects samples based on their distribution, uncertainty, and redundancy. RRAAL includes a representation generator, state discriminator, and redundancy removal module (RRM). The purpose of the representation generator is to learn the feature representation of a sample, and the state discriminator predicts the state of the feature vector after concatenation. We added a sample discriminator to the representation generator to improve the representation learning ability of the generator and designed a norm online uncertainty indicator (Norm-OUI) to provide a more accurate uncertainty score for the state discriminator. In addition, we designed an RRM based on a greedy algorithm to reduce the number of redundant samples in the labelled pool. The experimental results on four datasets show that the state discriminator, Norm-OUI, and RRM can improve the performance of RRAAL, and RRAAL outperforms the previous state-of-the-art active learning methods.
Highlights
In recent years, image processing tasks based on deep learning [1,2,3] have achieved great success, but they mainly rely on a large number of labelled datasets
SRAAL sets up an online uncertainty indicator (OUI) for the unlabelled samples to calculate the contribution of this sample to the model. e OUI considers the influence of the maximum element and variance in the category vector on the uncertainty
On the CIFAR-10 and Cityscapes datasets, the experimental results show that the overall performance of removal adversarial active learning (RRAAL) is always better than that of the other methods, and the performance of RRAAL without both is slightly better than that of SRAAL and lower than that of the other three methods
Summary
Image processing tasks based on deep learning [1,2,3] have achieved great success, but they mainly rely on a large number of labelled datasets. SRAAL sets up an online uncertainty indicator (OUI) for the unlabelled samples to calculate the contribution of this sample to the model. For a dataset with 10 categories, the variances of the vectors [0.6, 0.4, 0, 0, 0, 0, 0, 0, 0, 0] and [0.5, 0.5, 0, 0, 0, 0, 0, 0, 0, 0] are 0.42 and 0.40, respectively, which are very similar numerically, while their uncertainties are quite different. (1) We propose a redundancy removal adversarial active learning (RRAAL) method based on norm online uncertainty indicator, which fully considers the diversity, uncertainty, and redundancy of samples (2) We design a sample discriminator to improve the representational learning ability of the generator and proposed a Norm-OUI based on the p-norm to calculate the uncertainty score of the samples (3) We design an RRM to remove redundant samples and reduce inefficient labelling
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