arXiv cs.AI· Shiqi Zhang, Tuomas Virtanen·· 14 小时前AI 评分18
伪标签该与标注样本共享 mini-batch 吗？SSCAL 在有限训练成本下的半监督持续主动学习
Should Pseudo-Labels Share Mini-Batches with Labeled Examples? Semi-Supervised Continual Active Learning with Limited Training Cost
AI 导读
SSCAL 首次将半监督持续主动学习中的伪标签问题形式化为两个决策：哪些无标注样本带伪标签进入训练，以及它们占每个 mini-batch 的比例。该方法按无标注样本到标注近邻的距离接纳伪标签、限制其占比，并重加权标注样本损失以保持期望梯度贡献不变，在冻结嵌入上伪标签器可增量更新且无需额外前向。
正文 · 原文
Abstract:Active learning (AL) reduces annotation by querying informative examples. Continual active learning (CAL) also cuts the training cost, the total number of gradient steps, by training one model across AL rounds with a fixed number of mini-batch steps per round. Pseudo-labels add supervision without annotation, but they increase training cost. In CAL, where every round trains on a fixed number of mini-batches, whether and how pseudo-labeled examples should share these mini-batches with labeled examples is therefore open. We provide the first formalization of this problem as two decisions after each query, namely (i) which unlabeled examples should enter training with a pseudo-label and (ii) what share of each mini-batch they should take. Our method, semi-supervised continual active learning (SSCAL), works with any acquisition function. It admits pseudo-labels based on distances from unlabeled examples to labeled neighbors, caps their share, and reweights the loss on labeled examples to keep their expected gradient contribution unchanged. On frozen embeddings, its pseudo-labeler updates incrementally without extra model forward passes. Across two audio datasets, two frozen encoders, six acquisition functions and ten seeds, SSCAL ranks first on average among four protocols with equal training steps, answering the question affirmatively.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Sound (cs.SD); Audio and Speech Processing (eess.AS) |
| Cite as: | arXiv:2610.35345 [cs.LG] |
| (or arXiv:2610.35345v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.35345 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Shiqi Zhang [view email]
[v1]
Mon, 26 Oct 2026 12:37:21 UTC (54 KB)
来源：arXiv cs.AI · arxiv.org