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.
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Datasets
Oxford 102 Flower
FC100
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Methods
1x1 Convolution •
Average Pooling •
Batch Normalization •
Bottleneck Residual Block •
Concatenated Skip Connection •
Convolution •
Dense Block •
Dense Connections •
DenseNet •
Dropout •
Feedforward Network •
Global Average Pooling •
Kaiming Initialization •
Max Pooling •
ReLU •
Residual Block •
Residual Connection •
ResNet •
Softmax
