Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Towards Enabling Meta-Learning from Target Models
Authors: Su Lu, Han-Jia Ye, Le Gan, De-Chuan Zhan
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically verify the effectiveness of S/T protocol in a typical application of meta-learning, i.e., few-shot learning. |
| Researcher Affiliation | Academia | Su Lu Han-Jia Ye Le Gan De-Chuan Zhan State Key Laboratory for Novel Software Technology Nanjing University, Nanjing, 210023, China EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/njulus/ST. |
| Open Datasets | Yes | In this part, we evaluate our S/T protocol on two benchmark datasets, i.e., mini Image Net [24] and tiered Image Net [17]. |
| Dataset Splits | Yes | 10000 tasks are used for both meta-training and meta-testing. 500 tasks are used for meta-validation. There are 64 classes for meta-training, 16 classes for meta-validation, and 20 classes for meta-testing. |
| Hardware Specification | Yes | We run the experiment on an Nvidia Ge Force RTX 2080ti GPU and Intel(R) Xeon(R) Silver 4110 CPU. |
| Software Dependencies | No | The paper mentions models like Res Net-12 and common frameworks are implied by the algorithms used (e.g., MAML, Proto Net), but it does not specify explicit software dependencies with version numbers (e.g., PyTorch 1.9, Python 3.8). |
| Experiment Setup | Yes | For each task, we generate 10 support instances by uniformly sampling x in range [ 5, 5]. For S/Q protocol, we additionally sample 30 query instances for each task. We set λ to 0.8, a relatively large value, in most of experiments. We pre-train a Res Net-12 with a linear layer on the meta-training split of mini Image Net. |