Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extract

Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple Classifier

Recent papers have suggested that transfer learning can outperform sophisticated meta-learning methods for few-shot image classification. We take this hypothesis to its logical conclusion, and suggest the use of an ensemble of high-quality, pre-trained feature extractors for few-shot image classification.We show experimentally that a library of pre-trained feature extractors combined with a simple feed-forward network learned with an L2-regularizer can be an excellent option for solving cross-domain few-shot image classification. Our experimental results suggest that this simpler sample-efficient approach far outperforms several well-established meta-learning algorithms on a variety of few-shot tasks.

少量拍摄的图像分类：只需使用经过预训练的特征提取器库和简单的分类器

最近的论文表明，对于少拍图像分类，转移学习可以胜过复杂的元学习方法。我们将此假设推论为逻辑上的结论，并建议将高质量的，经过预训练的特征提取器集成用于少数镜头图像分类。.. 我们通过实验证明，将经过预训练的特征提取器库与通过L2稳压器学习到的简单前馈网络相结合，可以解决跨域少镜头图像分类。我们的实验结果表明，这种简单高效的采样方法远胜于在各种“少拍即发”任务上建立的多种元学习算法。 （阅读更多）