Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference
Few-shot learning (FSL) is an important and topical problem in computer vision that has motivated extensive research into numerous methods spanning from sophisticated meta-learning methods to simple transfer learning baselines. We seek to push the limits of a simple-but-effective pipeline for more r...
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Main Authors | , , , , |
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Format | Journal Article |
Language | English |
Published |
14.04.2022
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Subjects | |
Online Access | Get full text |
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Summary: | Few-shot learning (FSL) is an important and topical problem in computer
vision that has motivated extensive research into numerous methods spanning
from sophisticated meta-learning methods to simple transfer learning baselines.
We seek to push the limits of a simple-but-effective pipeline for more
realistic and practical settings of few-shot image classification. To this end,
we explore few-shot learning from the perspective of neural network
architecture, as well as a three stage pipeline of network updates under
different data supplies, where unsupervised external data is considered for
pre-training, base categories are used to simulate few-shot tasks for
meta-training, and the scarcely labelled data of an novel task is taken for
fine-tuning. We investigate questions such as: (1) How pre-training on external
data benefits FSL? (2) How state-of-the-art transformer architectures can be
exploited? and (3) How fine-tuning mitigates domain shift? Ultimately, we show
that a simple transformer-based pipeline yields surprisingly good performance
on standard benchmarks such as Mini-ImageNet, CIFAR-FS, CDFSL and Meta-Dataset.
Our code and demo are available at https://hushell.github.io/pmf. |
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DOI: | 10.48550/arxiv.2204.07305 |