  The recent advanced deep learning techniques have shown the promising results
in various domains such as computer vision and natural language processing. The
success of deep neural networks in supervised learning heavily relies on a
large amount of labeled data. However, obtaining labeled data with target
labels is often challenging due to various reasons such as cost of labeling and
privacy issues, which challenges existing deep models. In spite of that, it is
relatively easy to obtain data with \textit{inexact supervision}, i.e., having
labels/tags related to the target task. For example, social media platforms are
overwhelmed with billions of posts and images with self-customized tags, which
are not the exact labels for target classification tasks but are usually
related to the target labels. It is promising to leverage these tags (inexact
supervision) and their relations with target classes to generate labeled data
to facilitate the downstream classification tasks. However, the work on this is
rather limited. Therefore, we study a novel problem of labeled data generation
with inexact supervision. We propose a novel generative framework named as
ADDES which can synthesize high-quality labeled data for target classification
tasks by learning from data with inexact supervision and the relations between
inexact supervision and target classes. Experimental results on image and text
datasets demonstrate the effectiveness of the proposed ADDES for generating
realistic labeled data from inexact supervision to facilitate the target
classification task.
