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..
Free Lunch for Few-shot Learning: Distribution Calibration
Authors: Shuo Yang, Lu Liu, Min Xu
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the experiments, we show that a simple logistic regression classifier trained with our strategy can achieve state-of-the-art accuracy on three datasets. |
| Researcher Affiliation | Academia | Shuo Yang1, Lu Liu2, Min Xu1 1School of Electrical and Data Engineering, University of Technology Sydney, 2Australian Artificial Intelligence Institute, University of Technology Sydney EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Training procedure for an N-way-K-shot task |
| Open Source Code | Yes | The code is available at: https://github.com/Shuo Yang-1998/Few_ Shot_Distribution_Calibration |
| Open Datasets | Yes | We evaluate our distribution calibration strategy on mini Image Net (Ravi & Larochelle (2017)), tiered Image Net (Ren et al. (2018)) and CUB (Welinder et al. (2010)). |
| Dataset Splits | Yes | mini Image Net... split the dataset into 64 base classes, 16 validation classes, and 20 novel classes. |
| Hardware Specification | No | For feature extractor, we use the Wide Res Net (Zagoruyko & Komodakis, 2016) trained following previous work (Mangla et al. (2020)). |
| Software Dependencies | No | We use the LR and SVM implementation of scikit-learn (Pedregosa et al. (2011)) with the default settings. |
| Experiment Setup | Yes | Specifically, the number of generated features is 750; k = 2 and λ = 0.5. α is 0.21, 0.21 and 0.3 for mini Image Net, tiered Image Net and CUB, respectively. |