Deep semi-supervised regression via pseudo-label filtering and calibration

Semi -supervised learning (SSL) is a widely used model training paradigm that effectively utilizes a limited set of labeled data and a substantially larger pool of unlabeled data. Historically, the focus of SSL research has predominantly been on classification tasks, employing methods such as consistency regularization and pseudolabeling. However, the direct application of these methods to regression tasks presents significant challenges, primarily due to the complexities associated with evaluating the reliability of pseudo -labels in a regression context. This paper introduces SimRegMatch, a novel semi -supervised regression (SSR) framework devised to overcome this specific challenge, by combining an uncertainty -based filtering mechanism with a similaritybased pseudo -label calibration approach. The former component is tasked with discerning which unlabeled examples possess pseudo -labels of sufficient reliability, achieved through the estimation of uncertainty levels. The latter component then refines these pseudo -labels by propagating information from labeled to unlabeled examples, thereby enhancing the overall quality of the pseudo -labels. The efficacy of SimRegMatch was rigorously tested through experiments conducted on the publicly available AgeDB dataset, which is centered around age prediction, as well as on a practical regression problem focused on the detection of interior noise levels in automobiles using accelerometer data. When benchmarked against current state-of-the-art methods in semi -supervised regression, SimRegMatch exhibited notable improvements in regression performance. Additionally, a series of ablation studies were carried out to dissect and understand the specific elements of the framework that were instrumental in achieving these performance enhancements. SimRegMatch addresses a pivotal issue in semi -supervised regression - the assessment of regression pseudo -label reliability - and substantially elevates model performance. By combining the strengths of uncertainty estimation and pseudolabel calibration, SimRegMatch emerges as a robust and versatile framework with significant potential for broad applicability in various SSR scenarios. A PyTorch implementation is publicly available at https://github.com/ YongwonJo/SimRegMatch.