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Contextualizing Enhances Gradient Based Meta Learning
Machine Learning · Submitted 17 Jul 2020
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim We demonstrate how to equip meta learning methods with contextualizers and show that their use can significantly boost performance on a range of few shot learning datasets.
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The abstract, sentence by sentence
Meta learning methods have found success when applied to few shot classification problems, in which they quickly adapt to a small number of labeled examples. Prototypical representations, each representing a particular class, have been of particular importance in this setting, as they provide a compact form to convey information learned from the labeled examples. However, these prototypes are just one method of representing this information, and they are narrow in their scope and ability to classify unseen examples. We propose the implementation of contextualizers, which are generalizable prototypes that adapt to given examples and play a larger role in classification for gradient-based models. We demonstrate how to equip meta learning methods with contextualizers and show that their use can significantly boost performance on a range of few shot learning datasets. We also present figures of merit demonstrating the potential benefits of contextualizers, along with analysis of how models make use of them. Our approach is particularly apt for low-data environments where it is difficult to update parameters without overfitting. Our implementation and instructions to reproduce the experiments are available at https://github.com/naveace/proto-context.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Meta learning methods have found success when applied to few shot classification problems, in which they quickly adapt to a small number of labeled examples. | background 1.00 | 0.07 | 0.02 | asserted 0.99 |
| s1 | Prototypical representations, each representing a particular class, have been of particular importance in this setting, as they provide a compact form to convey information learned from the labeled examples. | background 1.00 | 0.07 | 0.02 | asserted 1.00 |
| s2 | However, these prototypes are just one method of representing this information, and they are narrow in their scope and ability to classify unseen examples. | gap 0.89 | 0.13 | 0.03 | asserted 1.00 |
| s3 | We propose the implementation of contextualizers, which are generalizable prototypes that adapt to given examples and play a larger role in classification for gradient-based models. | contribution 0.84 | 0.78 | 0.21 | asserted 0.98 |
| s4 | We demonstrate how to equip meta learning methods with contextualizers and show that their use can significantly boost performance on a range of few shot learning datasets. | result 0.66 | 0.96 | 0.72 | asserted 0.99 |
| s5 | We also present figures of merit demonstrating the potential benefits of contextualizers, along with analysis of how models make use of them. | contribution 0.80 | 0.94 | 0.08 | hedged possibility 0.57 |
| s6 | Our approach is particularly apt for low-data environments where it is difficult to update parameters without overfitting. | interpretation 0.93 | 0.39 | 0.08 | asserted 0.93 |
| s7 | Our implementation and instructions to reproduce the experiments are available at https://github.com/naveace/proto-context. | contribution 0.78 | 0.44 | 0.02 | asserted 0.97 |
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The paper
Original abstract
Meta learning methods have found success when applied to few shot classification problems, in which they quickly adapt to a small number of labeled examples. Prototypical representations, each representing a particular class, have been of particular importance in this setting, as they provide a compact form to convey information learned from the labeled examples. However, these prototypes are just one method of representing this information, and they are narrow in their scope and ability to classify unseen examples. We propose the implementation of contextualizers, which are generalizable prototypes that adapt to given examples and play a larger role in classification for gradient-based models. We demonstrate how to equip meta learning methods with contextualizers and show that their use can significantly boost performance on a range of few shot learning datasets. We also present figures of merit demonstrating the potential benefits of contextualizers, along with analysis of how models make use of them. Our approach is particularly apt for low-data environments where it is difficult to update parameters without overfitting. Our implementation and instructions to reproduce the experiments are available at https://github.com/naveace/proto-context.
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How the literature uses it
1 arXiv paper cite this in 1 sentence
Papers citing it
Cites it as background 1 paper
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Despite there are many works [ref] , exploring meta-learning for few-shot classification tasks, meta-learning based tracking method are few in the visual tracking community.
Context-aware Visual Tracking with Joint Meta-updating · Related work · 0.83
What it cites · 3 sentences without a confident label
Builds on 1 paper
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We take this approach one step further and construct task-specific initializations before fine-tuning occurs along the lines of [ref] .
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples · Related Work · 0.99
Uses the method or tool of 1 paper
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[equation] ProtoMAML then initializes the head of the network with weights equal to $2 𝐩^(a)$ and biases equal to $-𝐩^(a)^2_2$ for $a=1,…n$ [ref] as follows:
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples · Head Initializations · 0.96
Cites as background 1 paper
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To solve this fairly formidable problem, many fields have been explored including meta learning [ref] , metric learning [ref] , as well as more broad approaches [ref] .
Meta-Learning for Semi-Supervised Few-Shot Classification · Related Work · 1.00
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