Teacher Assistant-Based Knowledge Distillation Extracting Multi-level Features on Single Channel Sleep EEG
Authors: Heng Liang, Yucheng Liu, Haichao Wang, Ziyu Jia
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on two public sleep datasets (Sleep-EDF and ISRUC-III). Compared to the baseline methods, the results show that our knowledge distillation framework achieves state-of-the-art performance. Experimental results show that our knowledge distillation framework achieves SOTA performance compared to existing knowledge distillation methods. |
| Researcher Affiliation | Academia | 1 Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China 2 Tsinghua-Berkeley Shenzhen Institute, Shenzhen, China |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code is available at https:// github.com/Hychao Wang/Sleep KD. |
| Open Datasets | Yes | We evaluate our method on two public datasets: ISRUC-III [Khalighi et al., 2016] and Sleep-EDF [Kemp et al., 2018]. |
| Dataset Splits | Yes | We split the datasets into the train, validation, and test sets by a ratio of 8:1:1 on Sleep-EDF and ISRUC-III separately. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. It only mentions implementing models with 'Tensor Flow'. |
| Software Dependencies | No | The paper mentions implementing models with 'Tensor Flow' but does not specify a version number or any other software dependencies with their versions, which is required for reproducible description. |
| Experiment Setup | Yes | We use Adam as the optimizer in each experiment. In experiments of the CNN framework, we choose Salient Sleep Net as a representative. The learning rate of Salient Sleep Net is 0.001. The number of training epochs is 60 and the batch size is 8. The weights are α = 0.3, β = 0.2, γ = 0.4 and δ = 0.1. In experiments of the CNN and RNN framework, we choose Deep Sleep Net as a representative. Deep Sleep Net has a learning rate of 0.00001. The number of training epochs is 200 for Sleep EDF, 300 for ISRUC-III and the batch size is 20. The weights are α = 1.0, β = 0.1 and γ = δ = 1.0. |