A Unified Framework for Heterogeneous Semi-supervised Learning

Marzi Heidari, Abdullah Alchihabi, Hao Yan, Yuhong Guo

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Keywords: Semi-Supervised Learning

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Abstract:

In this work, we introduce a novel problem setup termed as Heterogeneous Semi-
Supervised Learning (HSSL), which presents unique challenges by bridging the
semi-supervised learning (SSL) task and the unsupervised domain adaptation
(UDA) task, and expanding standard semi-supervised learning to cope with heterogeneous training data. At its core, HSSL aims to learn a prediction model
using a combination of labeled and unlabeled training data drawn separately from
heterogeneous domains that share a common set of semantic categories; this model
is intended to differentiate the semantic categories of test instances sampled from
both the labeled and unlabeled domains. In particular, the labeled and unlabeled
domains have dissimilar label distributions and class feature distributions. This
heterogeneity, coupled with the assorted sources of the test data, introduces significant challenges to standard SSL and UDA methods. Therefore, we propose a
novel method, Unified Framework for Heterogeneous Semi-supervised Learning
(Uni-HSSL), to address HSSL by directly learning a fine-grained classifier from the
heterogeneous data, which adaptively handles the inter-domain heterogeneity while
leveraging both the unlabeled data and the inter-domain semantic class relationships
for cross-domain knowledge transfer and adaptation. We conduct comprehensive
experiments and the experimental results validate the efficacy and superior performance of the proposed Uni-HSSL over state-of-the-art semi-supervised learning
and unsupervised domain adaptation methods.

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Submission Number: 6156