Finding Task-Relevant Features for Few-Shot Learning by Category Traversal
Few-shot learning is an important area of research. Conceptually, humans are readily able to understand new concepts given just a few examples, while in more pragmatic terms, limited-example training situations are common practice. Recent effective approaches to few-shot learning employ a metric-lea...
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| Vydáno v: | Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) s. 1 - 10 |
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01.06.2019
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| ISSN: | 1063-6919 |
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| Abstract | Few-shot learning is an important area of research. Conceptually, humans are readily able to understand new concepts given just a few examples, while in more pragmatic terms, limited-example training situations are common practice. Recent effective approaches to few-shot learning employ a metric-learning framework to learn a feature similarity comparison between a query (test) example, and the few support (training) examples. However, these approaches treat each support class independently from one another, never looking at the entire task as a whole. Because of this, they are constrained to use a single set of features for all possible test-time tasks, which hinders the ability to distinguish the most relevant dimensions for the task at hand. In this work, we introduce a Category Traversal Module that can be inserted as a plug-and-play module into most metric-learning based few-shot learners. This component traverses across the entire support set at once, identifying task-relevant features based on both intra-class commonality and inter-class uniqueness in the feature space. Incorporating our module improves performance considerably (5%-10% relative) over baseline systems on both miniImageNet and tieredImageNet benchmarks, with overall performance competitive with the most recent state-of-the-art systems. |
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| AbstractList | Few-shot learning is an important area of research. Conceptually, humans are readily able to understand new concepts given just a few examples, while in more pragmatic terms, limited-example training situations are common practice. Recent effective approaches to few-shot learning employ a metric-learning framework to learn a feature similarity comparison between a query (test) example, and the few support (training) examples. However, these approaches treat each support class independently from one another, never looking at the entire task as a whole. Because of this, they are constrained to use a single set of features for all possible test-time tasks, which hinders the ability to distinguish the most relevant dimensions for the task at hand. In this work, we introduce a Category Traversal Module that can be inserted as a plug-and-play module into most metric-learning based few-shot learners. This component traverses across the entire support set at once, identifying task-relevant features based on both intra-class commonality and inter-class uniqueness in the feature space. Incorporating our module improves performance considerably (5%-10% relative) over baseline systems on both miniImageNet and tieredImageNet benchmarks, with overall performance competitive with the most recent state-of-the-art systems. |
| Author | Eigen, David Wang, Xiaogang Li, Hongyang Dodge, Samuel Zeiler, Matthew |
| Author_xml | – sequence: 1 givenname: Hongyang surname: Li fullname: Li, Hongyang organization: The Chinese Univ. of Hong Kong – sequence: 2 givenname: David surname: Eigen fullname: Eigen, David organization: Clarifai Inc – sequence: 3 givenname: Samuel surname: Dodge fullname: Dodge, Samuel organization: Clarifai Inc – sequence: 4 givenname: Matthew surname: Zeiler fullname: Zeiler, Matthew organization: Clarifai Inc – sequence: 5 givenname: Xiaogang surname: Wang fullname: Wang, Xiaogang organization: Chinese Univ. of Hong Kong |
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| Snippet | Few-shot learning is an important area of research. Conceptually, humans are readily able to understand new concepts given just a few examples, while in more... |
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| SubjectTerms | Benchmark testing Computer vision Deep Learning Feature extraction Few shot learning Hands Pattern recognition Pragmatics Training |
| Title | Finding Task-Relevant Features for Few-Shot Learning by Category Traversal |
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