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- KIKUCHI Toshiki
- Keio University
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- OZASA Yuko
- Keio University
書誌事項
- タイトル別名
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- <b>A Model Ensemble Approach for Few-Shot Learning Using Aggregated Classifiers</b>
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抄録
<p>Despite the recent success in deep neural networks on the visual domain, we need a large amount of data to train the networks. Previous works addressed this issue as the few-shot learning which is the task to identify the class of an example in new classes not seen in a training phase with only a few examples of each new class. Some methods performed well on the few-shot tasks, but need a complex architecture and/or specialized loss functions, such as metric loss, meta learner, and memory. In this paper,we evaluate the performance of the ensemble approach aggregating a huge number of simple neural network models (up to 128 models) on standard few-shot datasets. Surprisingly, although the approach is simple, our experimental results show that the ensemble approach is competitive with state-of-the-art methods among similar architecture methods in some settings.</p>
収録刊行物
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- IIEEJ Transactions on Image Electronics and Visual Computing
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IIEEJ Transactions on Image Electronics and Visual Computing 7 (2), 97-105, 2019-12-15
一般社団法人 画像電子学会
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詳細情報 詳細情報について
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- CRID
- 1390850490583961984
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- NII論文ID
- 130008012590
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- NII書誌ID
- AA12661628
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- ISSN
- 21881901
- 2188191X
- 21881898
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- NDL書誌ID
- 030259934
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
- CiNii Articles
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可