Semi-supervised classification is a great focus of interest, as in real-world scenarios obtaining labels is expensive, time-consuming and might require expert knowledge. This has motivated the fast development of semi-supervised techniques, whose performance is on a par with or better than supervised approaches. A current major challenge for semi-supervised techniques is how to better handle the network calibration and confirmation bias problems for improving performance. In this work, we argue that energy models are an effective alternative to such problems. With this motivation in mind, we propose a hybrid framework for semi-supervised classification called CREPE model (1-Lapla$\mathbf{C}$ian g$\mathbf{R}$aph $\mathbf{E}$nergy for $\mathbf{P}$seudo-lab$\mathbf{E}$ls). Firstly, we introduce a new energy model based on the non-smooth $\ell_1$ norm of the normalised graph 1-Laplacian. Our functional enforces a sufficiently smooth solution and strengthens the intrinsic relation between the labelled and unlabelled data. Secondly, we provide a theoretical analysis for our proposed scheme and show that the solution trajectory does converge to a non-constant steady point. Thirdly, we derive the connection of our energy model for pseudo-labelling. We show that our energy model produces more meaningful pseudo-labels than the ones generated directly by a deep network. We extensively evaluate our framework, through numerical and visual experiments, using six benchmarking datasets for natural and medical images. We demonstrate that our technique reports state-of-the-art results for semi-supervised classification.
翻译:半监督分类是一个令人感兴趣的焦点,因为在现实世界中获得标签的假设情景中,获得标签是昂贵的、耗时的,可能需要专家知识。这促使了半监督技术的快速发展,其性能与监督方法相同或更好。目前半监督技术的主要挑战是如何更好地处理网络校准和确认偏差问题来改善性能。在这项工作中,我们争论能源模型是解决这些问题的有效替代物。出于这一动机,我们提议了一个半监督分类的半监督的准标准化框架,称为CREPE模型(1-Lapa$\mathbf{C}$$G\mathbf{R}。这促使了半监督技术的快速发展,半监督技术的效绩优于或优于监督方法。为了我们正常的图1-Laplecian的值分类标准(1-Laplax 1-Lacian ), 我们的功能性能操作性地执行一种足够平稳的直观的直观的直观图像, 并且加强我们不断的内向的能源模型, 我们的内向的内向数据显示一个我们 的内向的模型。