TaskNorm: Rethinking Batch Normalization for Meta-Learning
Authors: John Bronskill, Jonathan Gordon, James Requeima, Sebastian Nowozin, Richard Turner
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on fourteen datasets demonstrate that the choice of batch normalization has a dramatic effect on both classification accuracy and training time for both gradient basedand gradientfree meta-learning approaches. Importantly, TASKNORM is found to consistently improve performance. |
| Researcher Affiliation | Collaboration | 1University of Cambridge 2Invenia Labs 3Microsoft Research. |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Source code is available at https://github.com/ cambridge-mlg/cnaps |
| Open Datasets | Yes | We provide experiments with MAML running simple, but widely used few-shot learning tasks from the Omniglot (Lake et al., 2011) and mini Imagenet (Ravi & Larochelle, 2017). Next, we evaluate NLs on a demanding few-shot classification challenge called Meta-Dataset, composed of thirteen (eight train, five test) image classification datasets (Triantafillou et al., 2020). |
| Dataset Splits | No | The paper mentions 'validation accuracy' and 'meta-training set' and 'meta-test phase', and that 'A task τ is drawn from p(τ) and randomly split into a context set Dτ and target set T τ', but it does not provide specific percentages or sample counts for train/validation/test splits of the datasets themselves in the main body. Details are deferred to Appendix B, which is not accessible. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments, such as specific GPU or CPU models. |
| Software Dependencies | No | The paper mentions general frameworks like TensorFlow and PyTorch but does not provide specific version numbers for these or any other software dependencies needed to replicate the experiment. |
| Experiment Setup | No | The paper states that 'Configuration and training details can be found in Appendix B' and 'Details provided in Appendix B and Triantafillou et al. (2020)', but these specific experimental setup details (e.g., hyperparameters) are not provided in the main text. |