用不变/等变半监督学习提升少标签多任务模型性能
Multi-task learning on partially labeled datasets via invariant/equivariant semi-supervised learning
- 基于FixMatch及其等变扩展Dense FixMatch，实现多任务半监督学习
- 在少量标注数据下，性能显著优于监督基线，尤其在标注稀疏时
- 适合资源受限场景下的视觉多任务建模，如自动驾驶感知
我们研究了不变性与等变性半监督学习在处理部分标注数据集上多任务模型训练的潜力，其中各任务输出结构不同。具体采用流行的FixMatch方法进行不变性半监督学习及其等变扩展Dense FixMatch。在计算机视觉中常见的目标检测与语义分割任务上，评估其在Cityscapes和BDD100K数据集上的表现，考虑每项任务标注子集大小不同以及标注重叠程度差异。结果显示，无论是不变性还是等变性半监督学习，在多数情况下均优于监督基线，尤其在任务标注样本较少时提升最显著，且等变方法总体表现更优。研究表明，不变/等变学习是有限标注数据下多任务学习的一个有前景方向。
原文摘要 · Abstract (English)
We investigate the potential of invariant and equivariant semi-supervised learning for addressing the challenges of training multi-task models on partially labeled datasets with differently structured output tasks. Specifically, we use the popular FixMatch method for invariant semi-supervised learning and its equivariant extension Dense FixMatch. We evaluate their performance on the Cityscapes and BDD100K datasets in the context of the prevalent object detection and semantic segmentation tasks in computer vision. We consider varying sizes of the subsets annotated for each task and different overlaps among them. Our results for both invariant and equivariant semi-supervised learning outperform supervised baselines in most situations, with the most significant improvements observed when fewer labeled samples are available for a task and generally better results for the latter approach. Our study suggests that invariant/equivariant learning is a promising general direction for multi-task learning from limited labeled data.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。