A Baseline for Few-Shot Image ClassificationDownload PDF

25 Sept 2019, 19:25 (modified: 10 Feb 2022, 11:41)ICLR 2020 Conference Blind SubmissionReaders: Everyone
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Data: [CIFAR-FS](https://paperswithcode.com/dataset/cifar-fs), [FC100](https://paperswithcode.com/dataset/fc100), [ImageNet](https://paperswithcode.com/dataset/imagenet), [tieredImageNet](https://paperswithcode.com/dataset/tieredimagenet)
TL;DR: Transductive fine-tuning of a deep network is a strong baseline for few-shot image classification and outperforms the state-of-the-art on all standard benchmarks.
Abstract: Fine-tuning a deep network trained with the standard cross-entropy loss is a strong baseline for few-shot learning. When fine-tuned transductively, this outperforms the current state-of-the-art on standard datasets such as Mini-ImageNet, Tiered-ImageNet, CIFAR-FS and FC-100 with the same hyper-parameters. The simplicity of this approach enables us to demonstrate the first few-shot learning results on the ImageNet-21k dataset. We find that using a large number of meta-training classes results in high few-shot accuracies even for a large number of few-shot classes. We do not advocate our approach as the solution for few-shot learning, but simply use the results to highlight limitations of current benchmarks and few-shot protocols. We perform extensive studies on benchmark datasets to propose a metric that quantifies the "hardness" of a few-shot episode. This metric can be used to report the performance of few-shot algorithms in a more systematic way.
Keywords: few-shot learning, transductive learning, fine-tuning, baseline, meta-learning
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