Labeled Data Generation with Inexact Supervision
Labeled Data Generation with Inexact Supervision
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DOI：
10.1145/3447548.3467306
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发表时间：
2021-06
期刊：
影响因子：
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通讯作者：
Enyan Dai;Kai Shu;Yiwei Sun;Suhang Wang
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文献类型：
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作者：
Enyan Dai;Kai Shu;Yiwei Sun;Suhang Wang
最近先进的深度学习技术在计算机视觉和自然语言处理等各个领域都显示出了可喜的成果。深度神经网络在监督学习中的成功在很大程度上依赖于大量的标记数据。然而，由于标签成本和隐私问题等各种原因，获得具有目标标签的标记数据通常具有挑战性，这对现有的深度模型提出了挑战。尽管如此，在不精确的监督下获得数据还是相对容易的，即，具有与目标任务相关的标签/标记。例如，社交媒体平台上充斥着数十亿个带有自定义标签的帖子和图像，这些标签并不是目标分类任务的确切标签，但通常与目标标签相关。利用这些标签（不精确监督）及其与目标类别的关系来生成标记数据以促进下游分类任务是有希望的。然而，这方面的工作相当有限。因此，我们研究了一个新的问题的标记数据生成与不精确的监督。我们提出了一种新的生成框架，名为ADDES，可以合成高质量的标记数据的目标分类任务，通过学习数据与不精确的监督和不精确的监督和目标类之间的关系。图像和文本数据集上的实验结果表明，所提出的ADDES的有效性产生现实的标记数据从不精确的监督，以促进目标分类任务。
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 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.