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..
Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile
Authors: Dong Chen, Lingfei Wu, Siliang Tang, Xiao Yun, Bo Long, Yueting Zhuang
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that Eigen-Reptile significantly outperforms the baseline, Reptile, by 22.93% and 5.85% on the corrupted and clean dataset, respectively. 5. Experimental Results and Discussion |
| Researcher Affiliation | Collaboration | 1College of Computer Science and Technology, Zhe Jiang University, Hang Zhou, China 2JD.COM Silicon Valley Research Center, 675 E Middlefield Rd, Mountain View, CA 94043 USA. |
| Pseudocode | Yes | Algorithm 1 Eigen-Reptile |
| Open Source Code | Yes | The code and data for the proposed method are provided for research purposes https://github.com/Anfeather/Eigen-Reptile. |
| Open Datasets | Yes | We verify the effectiveness of Eigen-Reptile alleviate overfitting sampling noise on two clean few-shot classification datasets Mini-Imagenet (Vinyals et al., 2016) and CIFAR-FS (Bertinetto et al., 2018). The Mini-Imagenet dataset contains 100 classes, each with 600 images. We follow (Ravi & Larochelle, 2016) to divide the dataset into three disjoint subsets: meta-training set, meta-validation set, and meta-testing set with 64 classes, 16 classes, and 20 classes, respectively. |
| Dataset Splits | Yes | We follow (Ravi & Larochelle, 2016) to divide the dataset into three disjoint subsets: meta-training set, meta-validation set, and meta-testing set with 64 classes, 16 classes, and 20 classes, respectively. |
| Hardware Specification | Yes | All experiments run on a 2080 Ti. |
| Software Dependencies | No | The paper mentions software like Adam and PyTorch but does not specify their version numbers or other software dependencies with version details. |
| Experiment Setup | Yes | All meta-learners use the same regressor that is trained for 30000 iterations with inner loop steps 5, batch size 10, and a fixed inner loop learning rate of 0.02. Our model is trained for 100000 iterations with a fixed inner loop learning rate of 0.0005, 7 inner-loop steps and batch size 10. |