Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels
Authors: Massimiliano Patacchiola, Jack Turner, Elliot J. Crowley, Michael O'Boyle, Amos J. Storkey
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically demonstrate that DKT outperforms several state-of-the-art algorithms in few-shot classification, and is the state of the art for cross-domain adaptation and regression. |
| Researcher Affiliation | Academia | Massimiliano Patacchiola School of Informatics University of Edinburgh mpatacch@ed.ac.uk Jack Turner School of Informatics University of Edinburgh jack.turner@ed.ac.uk Elliot J. Crowley School of Engineering University of Edinburgh elliot.j.crowley@ed.ac.uk Michael O Boyle School of Informatics University of Edinburgh mob@inf.ed.ac.uk Amos Storkey School of Informatics University of Edinburgh a.storkey@ed.ac.uk |
| Pseudocode | Yes | Algorithm 1 Deep Kernel Transfer (DKT) in the few-shot setting, train and test functions. |
| Open Source Code | Yes | We derive two versions of DKT for both the regression and the classification setting, comparing it against recent methods on a standardized benchmark environment; the code is released with an open-source license1. 1https://github.com/Bayes Watch/deep-kernel-transfer |
| Open Datasets | Yes | We consider two challenging datasets: the Caltech-UCSD Birds (CUB-200, Wah et al., 2011), and mini-Image Net (Ravi and Larochelle, 2017). |
| Dataset Splits | Yes | In the most common scenario, training, validation and test datasets each consist of distinct tasks sampled from the same overall distribution over tasks. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments, only discussing the software framework used. |
| Software Dependencies | No | The paper mentions 'Py Torch and GPy Torch' but does not provide specific version numbers for these or any other software components, which are necessary for reproducibility. |
| Experiment Setup | Yes | The training set is composed of 5 support and 5 query points, and the test set of 5 support and 200 query points. All the experiments are 5-way (5 random classes) with 1 or 5-shot (1 or 5 samples per class in the support set). A total of 16 samples per class are provided for the query set. |