Context Consistency Regularization for Label Sparsity in Time Series
Authors: Yooju Shin, Susik Yoon, Hwanjun Song, Dongmin Park, Byunghyun Kim, Jae-Gil Lee, Byung Suk Lee
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that the proposed framework outperforms the existing state-of-the-art consistency regularization frameworks through comprehensive experiments on real-world time-series datasets. |
| Researcher Affiliation | Collaboration | 1Graduate School of Data Science, KAIST, Korea 2Department of Computer Science, University of Illinois at Urbana-Champaign, USA 3AWS AI Labs, USA 4School of Computing, KAIST, Korea 5Department of Computer Science, University of Vermont, USA. |
| Pseudocode | Yes | Algorithm 1 describes how Cross Match works in time-series consistency regularization. |
| Open Source Code | Yes | The source code is provided at https://github.com/ kaist-dmlab/Cross Match. |
| Open Datasets | Yes | We use three widely-used benchmark datasets in Table 1. HAPT is a sensor time-series dataset tracking human movements in a laboratory sampled with the frequency of 50Hz (Anguita et al., 2013). m Health is a similar action recognition dataset recorded with more wearable sensors, such as 3D accelerometers, 3D gyroscopes, 3D magnetometers, and electrodes, whose sampling frequency is 50Hz (Banos et al., 2014). Opportunity is a collection of sensor recordings at 100Hz capturing daily natural human activities with wearable, object, and ambient sensors (Roggen et al., 2010). |
| Dataset Splits | Yes | We measure timestamp accuracy and segmental F1 score with five-fold cross validation and report the average value with standard deviation of five runs. |
| Hardware Specification | Yes | For every experiment, we use Intel(R) Xeon(R) Gold 6226R CPU @ 2.90GHz and NVIDIA RTX 3090. |
| Software Dependencies | No | The paper mentions using 'MS-TCN as the backbone sequential classifier' and 'SGD optimizer', but does not specify version numbers for these or other software dependencies (e.g., Python, PyTorch/TensorFlow versions). |
| Experiment Setup | Yes | For Cross Match, we set the confidence threshold τ to 0.95 and the weight of the unlabeled loss λ to 1. The model is first trained without any pseudo-labels, i.e., only using the labeled batches. We start to update a model with pseudo-labels after the number of pseudo-labels in each class for a batch is balanced. Formally, this condition is satisfied when the entropy of the numbers of pseudo-labels per class is above 0.99 for the last 100 iterations... Table 6. Training hyperparameters. Stage: 4, Layer: 11, BL: 4, BU: 8, Optimizer: SGD, Momentum: 0.9, Nesterov: True, η: 0.005, Scheduling: cos(7πi/2I). |