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..
Interventional Few-Shot Learning
Authors: Zhongqi Yue, Hanwang Zhang, Qianru Sun, Xian-Sheng Hua
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conducted experiments on benchmark datasets in FSL literature: 1) mini Image Net [62] containing 600 images per class over 100 classes. We followed the split proposed in [48]: 64/16/20 classes for train/val/test. ... Table 1: Acc (%) averaged over 2000 5-way FSL tasks before and after applying IFSL. ... Overall, our IFSL achieves the new state-of-the-art on both datasets. |
| Researcher Affiliation | Collaboration | Zhongqi Yue1,3, Hanwang Zhang1, Qianru Sun2, Xian-Sheng Hua3 1Nanyang Technological University, 2Singapore Management University, 3Alibaba Group |
| Pseudocode | No | The paper describes its algorithmic implementations but does not include structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Code is released at https://github. com/yue-zhongqi/ifsl. |
| Open Datasets | Yes | We conducted experiments on benchmark datasets in FSL literature: 1) mini Image Net [62]... 2) tiered Image Net [49]... 3) Caltech-UCSD Birds-200-2011 (CUB) [65] for crossdomain evaluation. |
| Dataset Splits | Yes | We followed the split proposed in [48]: 64/16/20 classes for train/val/test. |
| Hardware Specification | No | The paper mentions 'donations of GPUs' in the acknowledgements, but does not provide specific details about the hardware used for experiments, such as GPU models, CPU types, or memory. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | Training and evaluation settings on mini Image Net and tiered Image Net are included in Appendix 5. (Appendix 5 states: 'In all experiments, we used Adam optimizer [31] with an initial learning rate of 1e-3, which decays by 0.5 every 25 epochs up to 75 epochs. The batch size is 16.') |