Ma Handong, Li Changsheng, Shi Xinchu, Yuan Ye, Wang Guoren
IEEE Trans Neural Netw Learn Syst. 2024 Feb;35(2):2894-2900. doi: 10.1109/TNNLS.2022.3190420. Epub 2024 Feb 5.
Recently, deep learning has been successfully applied to unsupervised active learning. However, the current method attempts to learn a nonlinear transformation via an auto-encoder while ignoring the sample relation, leaving huge room to design more effective representation learning mechanisms for unsupervised active learning. In this brief, we propose a novel deep unsupervised active learning model via learnable graphs, named ALLGs. ALLG benefits from learning optimal graph structures to acquire better sample representation and select representative samples. To make the learned graph structure more stable and effective, we take into account k -nearest neighbor graph as a priori and learn a relation propagation graph structure. We also incorporate shortcut connections among different layers, which can alleviate the well-known over-smoothing problem to some extent. To the best of our knowledge, this is the first attempt to leverage graph structure learning for unsupervised active learning. Extensive experiments performed on six datasets demonstrate the efficacy of our method.
最近，深度学习已成功应用于无监督主动学习。然而，当前的方法试图通过自动编码器学习非线性变换，却忽略了样本关系，这为设计更有效的无监督主动学习表示学习机制留下了很大空间。在本简报中，我们提出了一种通过可学习图的新型深度无监督主动学习模型，名为ALLGs。ALLG受益于学习最优图结构以获得更好的样本表示并选择有代表性的样本。为了使学习到的图结构更稳定有效，我们将k近邻图作为先验，并学习关系传播图结构。我们还在不同层之间引入了捷径连接，这可以在一定程度上缓解众所周知的过平滑问题。据我们所知，这是首次尝试利用图结构学习进行无监督主动学习。在六个数据集上进行的大量实验证明了我们方法的有效性。
