Contrastive Meta-Learning for Partially Observable Few-Shot Learning
Authors: Adam Jelley, Amos Storkey, Antreas Antoniou, Sam Devlin
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach on an adaptation of a comprehensive few-shot learning benchmark, Meta-Dataset, and demonstrate the benefits of POEM over other meta-learning methods at representation learning from partial observations. |
| Researcher Affiliation | Collaboration | Adam Jelley1, Amos Storkey1, Antreas Antoniou1, Sam Devlin2 1School of Informatics, University of Edinburgh, 2 Microsoft Research, Cambridge |
| Pseudocode | Yes | Full pseudocode for training POEM with this objective is provided in appendix A.3. |
| Open Source Code | Yes | Implementation code is available at https://github.com/AdamJelley/POEM |
| Open Datasets | Yes | To comprehensively evaluate our approach, we adapt a large-scale few-shot learning benchmark, Meta-Dataset (Triantafillou et al., 2020)... |
| Dataset Splits | Yes | on all of which our models were trained, validated and tested on according to the data partitions specified by the Meta-Dataset benchmark. |
| Hardware Specification | Yes | The Finetuning, Prototypical Network and POEM baselines were run on on-premise RTX2080 GPUs. MAML required more memory and compute than available, so was run on cloud A100s. |
| Software Dependencies | No | The paper mentions 'Py Torch (Paszke et al., 2019)' and 'Torchvision' but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | Parameters used for adapted PO-Meta-Dataset are provided in Table A.5. All parameters not listed chosen to match Meta-Dataset defaults. All augmentations are applied using Torchvision, with parameters specified. |