CALANet: Cheap All-Layer Aggregation for Human Activity Recognition
Authors: Jaegyun Park, Dae-Won Kim, Jaesung Lee
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Evaluated on seven publicly available datasets, CALANet outperformed existing methods, achieving state-of-the-art performance.In this section, we evaluate the superiority of CALANet. In Section 4.1, we describe the experimental setup. Section 4.2 presents the compared results of CALANet and other networks on seven HAR datasets. Section 4.3 provides an in-depth analysis via an ablation study. Lastly, Section 4.4 measures the actual inference time of CALANet. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Chung-Ang University, Republic of Korea 2Department of Artificial Intelligence, Chung-Ang University, Republic of Korea |
| Pseudocode | No | The paper describes the network architecture and theoretical derivations but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source codes of the CALANet are publicly available at https://github.com/jgpark92/CALANet. |
| Open Datasets | Yes | We used seven public benchmark datasets, including various sampling frequencies, the number of activities, and sensors. They include UCI-HAR [1], Uni Mi B-SHAR [30], DSADS [3], OPPORTUNITY [6], KU-HAR [47], PAMAP2 [46], and REALDISP [2]. The details for each dataset are described in Appendix F. |
| Dataset Splits | No | For each dataset, the paper specifies train and test splits (e.g., '70% and 30% of the dataset were used as the training and test sets, respectively' for UCI-HAR) but does not explicitly mention a separate validation dataset split. |
| Hardware Specification | Yes | To estimate the actual response time of our CALANet, we used the AMD Ryzen 7 5800X 8-Core Processor without the support of graphics processing units. |
| Software Dependencies | No | The paper mentions 'PyTorch [38]' and 'Adam optimizer [26]' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | Precisely, they were trained for 300 epochs with a batch size of 128 using a 2080Ti graphics-processing unit. We used the Adam optimizer [26] with β1 = 0.9, β2 = 0.999 and ϵ = 10-8, where the learning rate and weight decay were set to 0.0005. For the CALALet, we set Dk, N, and L to 5, 128, and 9, respectively. |