Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning
Meta-learning has been the most common framework for few-shot learning in recent years. It learns the model from collections of few-shot classification tasks, which is believed to have a key advantage of making the training objective consistent with the testing objective. However, some recent works...
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| Published in: | Proceedings / IEEE International Conference on Computer Vision pp. 9042 - 9051 |
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| Main Authors: | , , , , |
| Format: | Conference Proceeding |
| Language: | English |
| Published: |
IEEE
01.10.2021
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| Subjects: | |
| ISSN: | 2380-7504 |
| Online Access: | Get full text |
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| Summary: | Meta-learning has been the most common framework for few-shot learning in recent years. It learns the model from collections of few-shot classification tasks, which is believed to have a key advantage of making the training objective consistent with the testing objective. However, some recent works report that by training for whole-classification, i.e. classification on the whole label-set, it can get comparable or even better embedding than many meta-learning algorithms. The edge between these two lines of works has yet been underexplored, and the effectiveness of meta-learning in few-shot learning remains unclear. In this paper, we explore a simple process: meta-learning over a whole-classification pre-trained model on its evaluation metric. We observe this simple method achieves competitive performance to state-of-the-art methods on standard bench-marks. Our further analysis shed some light on understanding the trade-offs between the meta-learning objective and the whole-classification objective in few-shot learning. Our code is available at https://github.com/yinboc/few-shot-meta-baseline. |
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| ISSN: | 2380-7504 |
| DOI: | 10.1109/ICCV48922.2021.00893 |