Hacking Task Confounder in Meta-Learning
Authors: Jingyao Wang, Yi Ren, Zeen Song, Jianqi Zhang, Changwen Zheng, Wenwen Qiang
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on various benchmark datasets demonstrate that our work achieves state-of-the-art (SOTA) performance. |
| Researcher Affiliation | Academia | 1Institute of Software Chinese Academy of Sciences 2University of Chinese Academy of Sciences {wangjingyao23, renyi, songzeen, zhangjianqi, changwen, qiangwenwen}@iscas.ac.cn |
| Pseudocode | Yes | The pseudocode and pipeline of Meta CRL are shown in Appendix B. |
| Open Source Code | Yes | The code is provided in https://github. com/Wang Jingyao07/Meta CRL. |
| Open Datasets | Yes | Specifically, we first randomly sample 400 tasks from mini Image Net dataset [Vinyals et al., 2016] and divide them into a training set and a test set. |
| Dataset Splits | No | The paper mentions dividing tasks into a training set and a test set for mini ImageNet and does not explicitly state a separate validation split or its proportions. |
| Hardware Specification | No | The paper does not provide specific hardware details such as CPU, GPU models, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions implementing methods and using datasets but does not list specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | In this experiment, we set λ1 and λ2 to 0.4 and 0.2. (from Sinusoid Regression section) / In this experiment, we set λ1 and λ2 to 0.5 and 0.35, respectively. (from Image Classification section) / In this experiment, λ1 and λ2 are both set to 0.3 (from Drug Activity Prediction section) / In this experiment, the values of λ1 and λ2 are set to 0.3 and 0.2 (from Pose Prediction section). |