Unsupervised Learning via Meta-Learning
Authors: Kyle Hsu, Sergey Levine, Chelsea Finn
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments across four image datasets indicate that our unsupervised meta-learning approach acquires a learning algorithm without any labeled data that is applicable to a wide range of downstream classification tasks, improving upon the embedding learned by four prior unsupervised learning methods. |
| Researcher Affiliation | Academia | Kyle Hsu University of Toronto kyle.hsu@mail.utoronto.ca Sergey Levine, Chelsea Finn University of California, Berkeley {svlevine,cbfinn}@eecs.berkeley.edu |
| Pseudocode | Yes | Algorithm 1 CACTUs for classification |
| Open Source Code | Yes | Links to code for the experiments can be found at https://sites.google.com/view/unsupervised-via-meta. |
| Open Datasets | Yes | Across four image datasets (MNIST, Omniglot, mini Image Net, and Celeb A)" and in Appendix G: "ILSVRC 2012 dataset’s training split (Russakovsky et al., 2015) |
| Dataset Splits | Yes | We partition each dataset into meta-training, meta-validation, and meta-testing splits. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. It only mentions general setup like using 'architectures from prior work'. |
| Software Dependencies | No | The paper mentions optimizers like Adam (Kingma & Ba, 2014) and SGD, and states 'We build on the authors publicly available codebase found at https://github.com/cbfinn/maml.' and other similar statements for ProtoNets and embedding learning methods. However, it does not specify version numbers for any software dependencies like Python, PyTorch, TensorFlow, or specific library versions. |
| Experiment Setup | Yes | APPENDIX E HYPERPARAMETERS AND ARCHITECTURES, Table 5: MAML hyperparameter summary. Table 6: Proto Nets hyperparameter summary. Table 11: MAML hyperparameter summary for Image Net. |