swMATH ID: 41279
Software Authors: Boris N. Oreshkin, Pau Rodriguez, Alexandre Lacoste
Description: TADAM: Task dependent adaptive metric for improved few-shot learning. Few-shot learning has become essential for producing models that generalize from few examples. In this work, we identify that metric scaling and metric task conditioning are important to improve the performance of few-shot algorithms. Our analysis reveals that simple metric scaling completely changes the nature of few-shot algorithm parameter updates. Metric scaling provides improvements up to 14
Homepage: https://arxiv.org/abs/1805.10123
Source Code:  https://github.com/ElementAI/TADAM
Related Software: MixMatch; AutoAugment; ImageNet; AugMix; Adam; SGDR; SpecAugment; S4L; mixup; RandAugment; ReMixMatch; CIFAR; Python; FixMatch
Cited in: 0 Publications