Probabilistic Model-Agnostic Meta-Learning
Authors: Chelsea Finn, Kelvin Xu, Sergey Levine
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments The goal of our experimental evaluation is to answer the following questions: (1) can our approach enable sampling from the distribution over potential functions underlying the training data?, (2) does our approach improve upon the MAML algorithm when there is ambiguity over the class of functions?, and (3) can our approach scale to deep convolutional networks? We study two illustrative toy examples and a realistic ambiguous few-shot image classification problem. For the both experimental domains, we compare MAML to our probabilistic approach. |
| Researcher Affiliation | Academia | Chelsea Finn , Kelvin Xu , Sergey Levine UC Berkeley {cbfinn,kelvinxu,svlevine}@eecs.berkeley.edu |
| Pseudocode | Yes | Algorithm 1 Meta-training, differences from MAML in red; Algorithm 2 Meta-testing |
| Open Source Code | Yes | Additional qualitative results and code can be found at https://sites.google.com/view/probabilistic-maml/ |
| Open Datasets | Yes | We additionally provide a comparison on the Mini Imagenet benchmark and specify the hyperparameters in the supplementary appendix. We study an ambiguous extension to the celeb A attribute classification task. |
| Dataset Splits | No | Meta-learning algorithms proceed by sampling data from a given task, and splitting the sampled data into a set of a few datapoints, Dtr used for training the model and a set of datapoints for measuring whether or not training was effective, Dtest. The paper describes the use of Dtr and Dtest but does not provide specific train/validation/test dataset split percentages or counts. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | We additionally provide a comparison on the Mini Imagenet benchmark and specify the hyperparameters in the supplementary appendix. The specific hyperparameters are not provided in the main text. |