用相似性度量优化时间序列扩散模型训练，提升效率。
Time Series Similarity Score Functions to Monitor and Interact with the Training and Denoising Process of a Time Series Diffusion Model applied to a Human Activity Recognition Dataset based on IMUs
- 引入多种相似性指标监控扩散过程
- 减少训练轮次且保持分类性能不变
- 适配下游任务，适合时序生成研究者
去噪扩散概率模型能生成合成传感器信号。其训练由损失函数控制，衡量前向加噪过程与模型预测噪声之间的差异，从而生成真实数据。然而，过程随机性和损失函数本身使得数据质量难以评估。为此，我们考察多种相似性度量，并改进其中一种，通过这些度量监控训练与生成过程。该改进度量可基于输入数据微调，以满足底层分类任务需求。实验表明，可在不降低分类性能的前提下显著减少训练轮次。优化训练流程不仅节省资源，也缩短生成模型训练时间。
原文摘要 · Abstract (English)
Denoising diffusion probabilistic models are able to generate synthetic sensor signals. The training process of such a model is controlled by a loss function which measures the difference between the noise that was added in the forward process and the noise that was predicted by the diffusion model. This enables the generation of realistic data. However, the randomness within the process and the loss function itself makes it difficult to estimate the quality of the data. Therefore, we examine multiple similarity metrics and adapt an existing metric to overcome this issue by monitoring the training and synthetisation process using those metrics. The adapted metric can even be fine-tuned on the input data to comply with the requirements of an underlying classification task. We were able to significantly reduce the amount of training epochs without a performance reduction in the classification task. An optimized training process not only saves resources, but also reduces the time for training generative models.
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