Frozen Feature Augmentation for Few-Shot Image Classification
<|reference_start|>Frozen Feature Augmentation for Few-Shot Image Classification: 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.<|reference_end|>
@article{bär2024frozen,
    title={Frozen Feature Augmentation for Few-Shot Image Classification},
    author={Andreas B"ar, Neil Houlsby, Mostafa Dehghani, Manoj Kumar},
    journal={arXiv preprint arXiv:2403.10519},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.10519},
    primaryClass={cs.CV}
}