论文标题
是什么使对小规模可穿戴任务进行良好的对比度学习？
What Makes Good Contrastive Learning on Small-Scale Wearable-based Tasks?
论文作者
论文摘要
自我监督的学习建立了一个新的学习表征范式，其标签注释较少甚至没有标签。最近，大规模的对比学习模型取得了显着的进步，这些模型需要大量的计算资源，但是这些模型实际上并不是针对小规模任务的最佳选择。为了填补空白，我们旨在研究基于可穿戴的活动识别任务的对比学习。具体而言，我们从算法级别和任务级别的观点中对对比度学习进行了深入研究。对于算法级别的分析，我们将对比模型分解为几个关键组成部分，并进行严格的实验评估，以更好地了解对比学习背后的功效和理由。更重要的是，对于任务级分析，我们表明，基于可穿戴的信号为现有的对比模型带来了独特的挑战和机遇，而现有算法无法轻易解决这些挑战。我们彻底的实证研究表明了重要的实践，并阐明了未来的研究挑战。同时，本文介绍了一个开源的Pytorch库\ Texttt {Cl-Har}，该图书馆可以作为研究人员的实用工具。该库是高度模块化且易于使用的，这为将来迅速探索新颖的对比模型开辟了途径。
Self-supervised learning establishes a new paradigm of learning representations with much fewer or even no label annotations. Recently there has been remarkable progress on large-scale contrastive learning models which require substantial computing resources, yet such models are not practically optimal for small-scale tasks. To fill the gap, we aim to study contrastive learning on the wearable-based activity recognition task. Specifically, we conduct an in-depth study of contrastive learning from both algorithmic-level and task-level perspectives. For algorithmic-level analysis, we decompose contrastive models into several key components and conduct rigorous experimental evaluations to better understand the efficacy and rationale behind contrastive learning. More importantly, for task-level analysis, we show that the wearable-based signals bring unique challenges and opportunities to existing contrastive models, which cannot be readily solved by existing algorithms. Our thorough empirical studies suggest important practices and shed light on future research challenges. In the meantime, this paper presents an open-source PyTorch library \texttt{CL-HAR}, which can serve as a practical tool for researchers. The library is highly modularized and easy to use, which opens up avenues for exploring novel contrastive models quickly in the future.
