Active Batch Sampling for Multi-label Classification with Binary User Feedback
Active Batch Sampling for Multi-label Classification with Binary User Feedback
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DOI：
10.1109/wacv57701.2024.00252
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发表时间：
2024-01
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影响因子：
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通讯作者：
Debanjan Goswami;Shayok Chakraborty
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文献类型：
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作者：
Debanjan Goswami;Shayok Chakraborty
多标签分类是多类分类的推广，其中单个数据样本可以有多个标签。虽然深度神经网络在多标签学习方面表现出了值得称赞的性能，但它们需要大量手动注释的训练数据才能获得良好的泛化能力。然而，注释多标签数据样本需要人类预言机单独考虑每个类的存在/不存在，这是极其费力的。主动学习算法自动从大量未标记数据中识别显着实例和示例实例，并可有效减少诱导机器学习模型时的人工注释工作。在本文中，我们提出了一种用于多标签学习的新型主动学习框架，该框架查询一批（图像-标签）对，并针对每对提出所查询的标签是否存在于相应图像中的问题；人类注释者只需要提供二进制反馈（“是/否”）来响应每个查询，这涉及更少的手动工作。我们将图像和标签选择作为一个约束优化问题，并导出线性规划松弛来选择一批（图像-标签）对，这些对底层深度神经网络提供最大程度的信息。我们对三个具有挑战性的数据集进行了广泛的实证研究，证实了我们的方法在现实世界多标签分类应用中的潜力。
Multi-label classification is a generalization of multiclass classification, where a single data sample can have multiple labels. While deep neural networks have depicted commendable performance for multi-label learning, they require a large amount of manually annotated training data to attain good generalization capability. However, annotating a multi-label data sample requires a human oracle to consider the presence/absence of every single class individually, which is extremely laborious. Active learning algorithms automatically identify the salient and exemplar instances from large amounts of unlabeled data and are effective in reducing human annotation effort in inducing a machine learning model. In this paper, we propose a novel active learning framework for multi-label learning, which queries a batch of (image-label) pairs and for each pair, poses the question whether the queried label is present in the corresponding image; the human annotators merely need to provide a binary feedback ("yes/no") in response to each query, which involves much less manual work. We pose the image and label selection as a constrained optimization problem and derive a linear programming relaxation to select a batch of (image-label) pairs, which are maximally informative to the underlying deep neural network. Our extensive empirical studies on three challenging datasets corroborate the potential of our method for real-world multi-label classification applications.