Cost‐effective multi‐instance multilabel active learning

Abstract

Multi-instance multi-label (MIML) Active Learning (M2AL) aims to improve the learner while reducing the cost as much as possible by querying informative labels of complex bags composed of diverse instances. Existing M2AL solutions suffer high query costs for scrutinizing all relevant labels of MIML samples, querying excessive bag-label or instance-label pairs. To address these issues, a Cost-effective M2AL solution (CM2AL) is presented. CM2AL first selects the most informative bag-label pairs by leveraging uncertainty, label correlations, label space sparsity, and informativeness from queried instances of the bag, and thus avoids scrutinizing all labels. Next, it queries the most probably positive instance-label pairs of the selected bag-label pair.Particularly, if the feedback is positive, the bag is positively annotated with the label. For negative feedback, it further leverages the label of the neighborhood bags and the label of the nearby instances of queried instances of this bag, if the suggested labels from bag-and instancelevels disagree, CM2AL temporally gives up querying this bag-label pair and moves to another most informativeness one; otherwise, it takes the agreed label to annotate the bag, which further saves the cost by avoiding the excessive query. Extensive experiments on MIML data sets from diverse domains show that CM2AL can more reduce the cost while managing a better performance than state-of-the-art methods, the collaboration between bags and instances contributes to the saved cost.

Improving Active Learning Performance through the Use of Data Augmentation

“…Research focused on AL has typically been focused on the specifcation of f acq [23] and domain-specifc applications, such as malware detection [24] or land use/land cover classifcation [25]. Acquisition functions can be divided into two diferent categories [26,27]:…”

A Cost-effective Deep Active Learning for Object Detection in Automated Driving Systems

“…Moreover, promising research directions have been explored to extend DAL algorithms regarding the integration of different annotation granularity, abundant unlabeled data and related supervision setting into active learning pipeline, including multi-label [19], multi-view [20], multi-instance [10], multi-instance multi-label (M2AL) [21], multi-view multi-instance multi-label (M3AL) [22], and unsupervised [23], [14] AL schemes. Among them, more attention has been paid to address two aspects: the automatic design of selection samples strategy [24] and the alleviation of various problems, namely data-related problems such as confidence and insufficient labeled sample, model-related problems such as generalization ability, and domain-specific problems such as domain shift, cold-start problem and class imbalance.…”

“…The query-driven approaches were based on gaining support from complementary techniques to perform query improvement, such as optimization techniques, metrics learning [25], and alternative learning paradigms (one-shot, contrastive, federated, goal-driven, domain adaptive...) [26], [27], [28], [29], [30]. On the other side, data-driven approaches were attempted to address several data-level perspectives in terms of data labeling supervision (weak, self, semi...) [11], [31], [32], [33], labeling setting (open-set recognition) [34], [35] and granularity [18], [21]. For further details please refer to the survey papers [7], [8], [36].…”