The goal of multi-label learning (MLL) is to associate a given instance with its relevant labels from a set of concepts. Previous works of MLL mainly focused on the setting where the concept set is assumed to be fixed, while many real-world applications require introducing new concepts into the set to meet new demands. One common need is to refine the original coarse concepts and split them into finer-grained ones, where the refinement process typically begins with limited labeled data for the finer-grained concepts. To address the need, we propose a special weakly supervised MLL problem that not only focuses on the situation of limited fine-grained supervision but also leverages the hierarchical relationship between the coarse concepts and the fine-grained ones. The problem can be reduced to a multi-label version of negative-unlabeled learning problem using the hierarchical relationship. We tackle the reduced problem with a meta-learning approach that learns to assign pseudo-labels to the unlabeled entries. Experimental results demonstrate that our proposed method is able to assign accurate pseudo-labels, and in turn achieves superior classification performance when compared with other existing methods. Multi-label learning (MLL) is an important learning problem with a wide range of applications BID2 BID0 BID11 . While traditional setting focuses on the scenario where the label classes are fixed before learning, many real-world applications face different situations. One scenario that is common in many applications is the growing number of classes BID13 , where the growth splits high-level concepts to finer-grained ones BID1 . For example, the set of classes might start from high-level concepts such as {Animal, . . ., Food }, and then grow to include finer-grained concepts like {Cat, . . ., Dog, . . ., Apple, . . ., Banana}. Typical applications may have collected sufficient number of labeled data for learning the high-level concepts in a fully supervised manner, but it can be challenging for the applications to efficiently adapt the classifier from the high-level (coarse-grained) concepts to the finer-grained ones. Conquering the challenge calls for two components: one is a strategic algorithm to actively collect a few fine-grained and informative labels, and the other is an effective learning model to exploit the fine-grained labels that have been partially collected.This work focuses on the design of the second component-learning an accurate fine-grained classifier with only limited supervision. In particular, we assume that the model receives a data set that contains all the coarse-grained labels and a few fine-grained ones, as shown in FIG0 . Then, the problem of constructing a predictive fine-grained model with the presented data set falls under the big umbrella of weakly supervised learning. Specifically, when we focus on leveraging the coarse-grained labels to build a fine-grained classifier, the problem resembles learning with inexact supervision considered by BID12 , where the coarse-grained labels are not in the exact form for the desired output and could only provide weak information about the target fine-grained labels. On the other hand, if we focus on using the fine-grained part of the labels to train the classifier, the problem can be viewed as a multi-label variant of learning with incomplete supervision as some instances receive their exact fine-grained ground-truth labels whereas some do not have labels at all BID12 . While both the aforementioned problems have attracted much research attention, the combination of them (inexact and incomplete supervision) which our problem of interest can be cast as, has not yet been carefully investigated to the best of our knowledge.Organization In this work, we start from a formal definition of our problem of interest. We then demonstrate a simple way to reduce the original problem into a special form of negative-unlabeled learning problem BID7 leveraging the label hierarchy. To tackle the reduced problem, we begin with a discussion on the caveats carried by some possible existing approaches, and propose a new model that undertakes the challenges posed by inexact and incomplete supervision through a novel learning to learn method which jointly exploits the hierarchical relationship between the coarse-and fine-grained labels, as well as the benefits of all available data in hand. The key idea within our model is to take into account all available information to learn the labeling assignments for the unlabeled entries, called pseudo-labels, and use them to guide the decent direction of the parameter updates on the underlying classifier. Finally, we experimentally demonstrate that the proposed method not only assigns accurate pseudo-labels to the unknown entries but also enjoys significantly better performance than other methods for learning fine-grained classifiers under the limited supervision setting. We design a tailored method through a meta-learning strategy, which learns to accurately assign pseudo-labels to the unknown entries of a special weakly supervised MLL problem. Experimental results show that our proposed method not only assigns accurate pseudo-labels, but also enable the underlying classifier learned to perform better than other possible existing solutions.
We propose a special weakly-supervised multi-label learning problem along with a newly tailored algorithm that learns the underlying classifier by learning to assign pseudo-labels.
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