Wensi Tang, Guodong Long, Lu Liu, Tianyi Zhou, Michael Blumenstein, Jing Jiang
We lifted 11 functions out of this paper's own repositories and ran 7 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| wensi-tang/os-cnn | canonical | 1 of 3 |
| Wensi-Tang/OS-CNN | — | 3 of 5 |
| Anonymouslink/OS-CNN | — | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| OS_CNN | Ran | Anonymouslink/OS-CNN/Classifiers/OS_CNN/OS_CNN.py pointer only (licence: NONE) · get_code("cd0ce30eebdcec3c") |
| SampaddingConv1D_BN | Ran | Anonymouslink/OS-CNN/Classifiers/OS_CNN/OS_CNN.py pointer only (licence: NONE) · get_code("0dee4616b6255680") |
| build_layer_with_layer_parameter | Ran | Anonymouslink/OS-CNN/Classifiers/OS_CNN/OS_CNN.py pointer only (licence: NONE) · get_code("fdf58bf8189b9e66") |
| calculate_mask_index | Ran | Wensi-Tang/OS-CNN/Classifiers/OS_CNN/OS_block.py code served (permissive licence) · get_code("5017468adad0af6b") |
| creak_layer_mask | Ran | wensi-tang/os-cnn/Classifiers/OS_CNN/OS_block.py code served (permissive licence) · get_code("5dadd23392f4ca8c") |
| creak_layer_mask | Ran | Wensi-Tang/OS-CNN/Classifiers/OS_CNN/OS_block.py code served (permissive licence) · get_code("b7f140a01a2fa577") |
| creat_mask | Ran | Wensi-Tang/OS-CNN/Classifiers/OS_CNN/OS_block.py code served (permissive licence) · get_code("2613c4915a16c36d") |
| OS_block | Not yet run | Wensi-Tang/OS-CNN/Classifiers/OS_CNN/OS_block.py code served (permissive licence) · get_code("c466ea8f0e598415") |
| build_layer_with_layer_parameter | Not yet run | Wensi-Tang/OS-CNN/Classifiers/OS_CNN/OS_block.py code served (permissive licence) · get_code("b6c70c06f06614b0") |
| calculate_mask_index | Not yet run | wensi-tang/os-cnn/Classifiers/OS_CNN/OS_block.py code served (permissive licence) · get_code("3b08b25c6a4703e0") |
| creat_mask | Not yet run | wensi-tang/os-cnn/Classifiers/OS_CNN/OS_block.py code served (permissive licence) · get_code("a14cbea422c27566") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The Receptive Field (RF) size has been one of the most important factors for One Dimensional Convolutional Neural Networks (1D-CNNs) on time series classification tasks. Large efforts have been taken to choose the appropriate size because it has a huge influence on the performance and differs significantly for each dataset. In this paper, we propose an Omni-Scale block (OS-block) for 1D-CNNs, where the kernel sizes are decided by a simple and universal rule. Particularly, it is a set of kernel sizes that can efficiently cover the best RF size across different datasets via consisting of multiple prime numbers according to the length of the time series. The experiment result shows that models with the OS-block can achieve a similar performance as models with the searched optimal RF size and due to the strong optimal RF size capture ability, simple 1D-CNN models with OS-block achieves the state-of-the-art performance on four time series benchmarks, including both univariate and multivariate data from multiple domains. Comprehensive analysis and discussions shed light on why the OS-block can capture optimal RF sizes across different datasets. Code available here 1
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2002.10061")
get_code_for_paper("2002.10061")
have("2002.10061")
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