In this paper, we study weakly supervised learning where a large amount of data supervision is not accessible. This includes 4 i) incomplete supervision, where only a small subset of labels is given, such as semi-supervised learning and domain adaptation; ii) 5 inexact supervision, where only coarse-grained labels are given, such as multi-instance learning and iii) inaccurate supervision, where 6 the given labels are not always ground-truth, such as label noise learning. Unlike supervised learning which typically achieves 7 performance improvement with more labeled examples, weakly supervised learning may sometimes even degenerate performance 8 with more weakly supervised data. Such deficiency seriously hinders the deployment of weakly supervised learning to real tasks. It is 9 thus highly desired to study safe weakly supervised learning, which never seriously hurts performance. To this end, we present a 10 generic ensemble learning scheme to derive a safe prediction by integrating multiple weakly supervised learners. We optimize the 11 worst-case performance gain and lead to a maximin optimization. This brings multiple advantages to safe weakly supervised learning. 12 First, for many commonly used convex loss functions in classification and regression, it is guaranteed to derive a safe prediction under 13 a mild condition. Second, prior knowledge related to the weight of the base weakly supervised learners can be flexibly embedded. 14 Third, it can be globally and efficiently addressed by simple convex quadratic or linear program. Finally, it is in an intuitive geometric 15 interpretation with the least square loss. Extensive experiments on various weakly supervised learning tasks, including semi-16 supervised learning, domain adaptation, multi-instance learning and label noise learning demonstrate our effectiveness.

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In this paper we study multi-label learning with weakly labeled data, i.e., labels of training examples are incomplete, which commonly occurs in real applications, e.g., image classification, document categorization. This setting includes, e.g., (i) semi-supervised multi-label learning where completely labeled examples are partially known; (ii) weak label learning where relevant labels of examples are partially known; (iii) extended weak label learning where relevant and irrelevant labels of examples are partially known. Previous studies often expect that the learning method with the use of weakly labeled data will improve the performance, as more data are employed. This, however, is not always the cases in reality, i.e., weakly labeled data may sometimes degenerate the learning performance. It is desirable to learn safe multi-label prediction that will not hurt performance when weakly labeled data is involved in the learning procedure. In this work we optimize multi-label evaluation metrics (F 1 score and Top-k precision) given that the ground-truth label assignment is realized by a convex combination of base multi-label learners. To cope with the infinite number of possible ground-truth label assignments, cutting-plane strategy is adopted to iteratively generate the most helpful label assignments. The whole optimization is cast as a series of simple linear programs in an efficient manner. Extensive experiments on three weakly labeled learning tasks, namely, (i) semi-supervised multi-label learning; (ii) weak label learning and (iii) extended weak label learning, clearly show that our proposal improves the safeness of using weakly labeled data compared with many state-of-the-art methods.

Long-Tailed Semi-Supervised Learning (LTSSL) aims to learn from class-imbalanced data where only a few samples are annotated. Existing solutions typically require substantial cost to solve complex optimization problems, or class-balanced undersampling which can result in information loss. In this paper, we present the TRAS (TRAnsfer and Share) to effectively utilize long-tailed semi-supervised data. TRAS transforms the imbalanced pseudo-label distribution of a traditional SSL model via a delicate function to enhance the supervisory signals for minority classes. It then transfers the distribution to a target model such that the minority class will receive significant attention. Interestingly, TRAS shows that more balanced pseudo-label distribution can substantially benefit minority-class training, instead of seeking to generate accurate pseudo-labels as in previous works. To simplify the approach, TRAS merges the training of the traditional SSL model and the target model into a single procedure by sharing the feature extractor, where both classifiers help improve the representation learning. According to extensive experiments, TRAS delivers much higher accuracy than state-of-the-art methods in the entire set of classes as well as minority classes. Code for TRAS is available at https:// github. com/ Stoma ch-ache/ TRAS.

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