Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters
Authors: Kaiyi Ji, Jason D. Lee, Yingbin Liang, H. Vincent Poor
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments on standard few-shot meta-learning benchmarks validate our theoretical findings. |
| Researcher Affiliation | Academia | Kaiyi Ji Department of ECE The Ohio State University ji.367@osu.edu Jason D. Lee Department of EE Princeton University jasonlee@princeton.edu Yingbin Liang Department of ECE The Ohio State University liang.889@osu.edu H. Vincent Poor Department of EE Princeton University poor@princeton.edu |
| Pseudocode | Yes | Algorithm 1 ANIL Algorithm |
| Open Source Code | No | The paper states: 'The experimental implementation and the model architectures are adapted from the existing repository [1] for ANIL.' Reference [1] is 'learn2learn, 2019. https://github.com/learnables/learn2learn.' This indicates use of an existing library, not the release of the authors' own specific implementation code for the methodology described in the paper. |
| Open Datasets | Yes | In this section, we validate our theory on the ANIL algorithm over two benchmarks for few-shot multiclass classification, i.e., FC100 [23] and mini Image Net [30]. |
| Dataset Splits | No | The paper states: 'We consider a 5-way 5-shot task on both the FC100 and mini Image Net datasets.' and 'We relegate the introduction of datasets, model architectures and hyper-parameter settings to Appendix A due to the space limitations.' However, the main text provided does not specify the train/validation/test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU model, CPU type, memory) used for running the experiments. |
| Software Dependencies | No | The paper states: 'The experimental implementation and the model architectures are adapted from the existing repository [1] for ANIL.' While reference [1] points to 'learn2learn' on GitHub, no specific version numbers for 'learn2learn' or any other software dependencies (e.g., Python, PyTorch) are provided. |
| Experiment Setup | Yes | We consider a 5-way 5-shot task on both the FC100 and mini Image Net datasets. We relegate the introduction of datasets, model architectures and hyper-parameter settings to Appendix A due to the space limitations. |