Frozen Feature Augmentation for Few-Shot Image Classification
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913446377816064 |
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| author | Bär, Andreas Houlsby, Neil Dehghani, Mostafa Kumar, Manoj |
| author_facet | Bär, Andreas Houlsby, Neil Dehghani, Mostafa Kumar, Manoj |
| contents | Training a linear classifier or lightweight model on top of pretrained vision model outputs, so-called 'frozen features', leads to impressive performance on a number of downstream few-shot tasks. Currently, frozen features are not modified during training. On the other hand, when networks are trained directly on images, data augmentation is a standard recipe that improves performance with no substantial overhead. In this paper, we conduct an extensive pilot study on few-shot image classification that explores applying data augmentations in the frozen feature space, dubbed 'frozen feature augmentation (FroFA)', covering twenty augmentations in total. Our study demonstrates that adopting a deceptively simple pointwise FroFA, such as brightness, can improve few-shot performance consistently across three network architectures, three large pretraining datasets, and eight transfer datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_10519 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Frozen Feature Augmentation for Few-Shot Image Classification Bär, Andreas Houlsby, Neil Dehghani, Mostafa Kumar, Manoj Computer Vision and Pattern Recognition Training a linear classifier or lightweight model on top of pretrained vision model outputs, so-called 'frozen features', leads to impressive performance on a number of downstream few-shot tasks. Currently, frozen features are not modified during training. On the other hand, when networks are trained directly on images, data augmentation is a standard recipe that improves performance with no substantial overhead. In this paper, we conduct an extensive pilot study on few-shot image classification that explores applying data augmentations in the frozen feature space, dubbed 'frozen feature augmentation (FroFA)', covering twenty augmentations in total. Our study demonstrates that adopting a deceptively simple pointwise FroFA, such as brightness, can improve few-shot performance consistently across three network architectures, three large pretraining datasets, and eight transfer datasets. |
| title | Frozen Feature Augmentation for Few-Shot Image Classification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.10519 |