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..
MetaNorm: Learning to Normalize Few-Shot Batches Across Domains
Authors: Yingjun Du, Xiantong Zhen, Ling Shao, Cees G. M. Snoek
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We verify its effectiveness by extensive evaluation on representative tasks suffering from the small batch and domain shift problems: few-shot learning and domain generalization. We further introduce an even more challenging setting: few-shot domain generalization. Results demonstrate that Meta Norm consistently achieves better, or at least competitive, accuracy compared to existing batch normalization methods. 4 EXPERIMENTAL RESULTS We conduct an extensive set of experiments on a total of 17 datasets containing more than 15 million images. |
| Researcher Affiliation | Collaboration | Yingjun Du1, Xiantong Zhen1,2, Ling Shao2, Cees G. M. Snoek1 1AIM Lab, University of Amsterdam 2Inception Institute of Artificial Intelligence |
| Pseudocode | Yes | In this Appendix we provide the detailed Meta Norm algorithm descriptions to conduct batch normalization for few-shot classification (Algorithm 1), domain generalization (Algorithm 2) and few-shot domain generalization (Algorithm 3). |
| Open Source Code | Yes | Our code will be publicly released.1 https://github.com/YDU-AI/Meta Norm. |
| Open Datasets | Yes | mini Image Net. The mini Image Net is originally proposed in (Vinyals et al., 2016) and has been widely used for evaluating few-shot learning algorithms. Omniglot. Omniglot (Lake et al., 2015) is a few-shot learning dataset... PACS (Li et al., 2017a) contains a total of 9,991 images... |
| Dataset Splits | Yes | We follow the train/val/ test split introduced in (Ravi & Larochelle, 2017), which uses 64 classes for meta-training, 16 classes for meta-validation, and the remaining 20 classes for meta-testing. |
| Hardware Specification | Yes | We implemented all models in the Tensorflow framework and tested on an NVIDIA Tesla V100. |
| Software Dependencies | No | The paper mentions 'implemented all models in the Tensorflow framework' but does not provide specific version numbers for TensorFlow or any other software dependencies. |
| Experiment Setup | Yes | For MAML experiments, we used the codebase by Finn (Finn, 2017). We use the Adam optimizer with default parameters, and a meta batch size of 4 tasks. The number of test episodes is set as 600. The number of training iterations is 60,000. We set λ=0.001. |