Multi-source transfer learning has been proven effective when within-target labeled data is scarce. Previous work focuses primarily on exploiting domain similarities and assumes that source domains are richly or at least comparably labeled. While this strong assumption is never true in practice, this paper relaxes it and addresses challenges related to sources with diverse labeling volume and diverse reliability. The first challenge is combining domain similarity and source reliability by proposing a new transfer learning method that utilizes both source-target similarities and inter-source relationships. The second challenge involves pool-based active learning where the oracle is only available in source domains, resulting in an integrated active transfer learning framework that incorporates distribution matching and uncertainty sampling. Extensive experiments on synthetic and two real-world datasets clearly demonstrate the superiority of our proposed methods over several baselines including state-of-the-art transfer learning methods.
: At the other end of the spectrum, active learning has been proven to be effective both empirically and theoretically. A new definition of sample complexity is proposed in @cite_13 to show that active learning is strictly better than passive learning asymptotically. In recent years, there has been increasing interests in combining transfer learning and active learning to jointly solve the issue of insufficient labeled data. A popular representative of this family is the work by @cite_19 where the JO-TAL method is proposed. It is a unified framework that jointly performs active learning and transfer learning by matching Hilbert space embeddings of distributions. A key benefit of these methods is to address the cold start problem of active learning by introducing a version space prior learned from transfer learning @cite_3 . However, unlike our approach, these methods all assume that source data are richly labeled and only perform active learning in the target domain.
@cite_19 @cite_13 @cite_3
2161033259 2029604420 1992946551
Active learning and transfer learning are two different methodologies that address the common problem of insufficient labels. Transfer learning addresses this problem by using the knowledge gained from a related and already labeled data source, whereas active learning focuses on selecting a small set of informative samples for manual annotation. Recently, there has been much interest in developing frameworks that combine both transfer and active learning methodologies. A few such frameworks reported in literature perform transfer and active learning in two separate stages. In this work, we present an integrated framework that performs transfer and active learning simultaneously by solving a single convex optimization problem. The proposed framework computes the weights of source domain data and selects the samples from the target domain data simultaneously, by minimizing a common objective of reducing distribution difference between the data set consisting of re-weighted source and the queried target domain data and the set of unlabeled target domain data. Comprehensive experiments on real data demonstrate the superior performance of the proposed approach. We describe and explore a new perspective on the sample complexity of active learning. In many situations where it was generally believed that active learning does not help, we show that active learning does help in the limit, often with exponential improvements in sample complexity. This contrasts with the traditional analysis of active learning problems such as non-homogeneous linear separators or depth-limited decision trees, in which Ω(1 ?) lower bounds are common. Such lower bounds should be interpreted carefully; indeed, we prove that it is always possible to learn an ?-good classifier with a number of samples asymptotically smaller than this. These new insights arise from a subtle variation on the traditional definition of sample complexity, not previously recognized in the active learning literature. We explore a transfer learning setting, in which a finite sequence of target concepts are sampled independently with an unknown distribution from a known family. We study the total number of labeled examples required to learn all targets to an arbitrary specified expected accuracy, focusing on the asymptotics in the number of tasks and the desired accuracy. Our primary interest is formally understanding the fundamental benefits of transfer learning, compared to learning each target independently from the others. Our approach to the transfer problem is general, in the sense that it can be used with a variety of learning protocols. As a particularly interesting application, we study in detail the benefits of transfer for self-verifying active learning; in this setting, we find that the number of labeled examples required for learning with transfer is often significantly smaller than that required for learning each target independently.