In this paper we reformulate few-shot classification as a reconstruction problem in latent space. The ability of the network to reconstruct a query feature map from support features of a given class predicts membership of the query in that class. We introduce a novel mechanism for few-shot classification by regressing directly from support features to query features in closed form, without introducing any new modules or large-scale learnable parameters. The resulting Feature Map Reconstruction Networks are both more performant and computationally efficient than previous approaches. We demonstrate consistent and substantial accuracy gains on four fine-grained benchmarks with varying neural architectures. Our model is also competitive on the non-fine-grained mini-ImageNet and tiered-ImageNet benchmarks with minimal bells and whistles.
翻译:在本文中,我们重新将微小的分类作为潜在空间的重建问题。网络根据某一类的支持特性重建查询地貌图的能力预测了该类的查询成员。我们引入了一个新机制,通过从支持特性直接退至封闭形式的查询特征,将微小的分类退缩,而没有引入任何新的模块或大规模可学习的参数。由此形成的地貌地图重建网络比以往的方法更具有性能和计算效率。我们展示了不同神经结构的四种精细筛选基准的一致和实质性的准确性增益。我们的模型还具有非果实微型图像网络和分层图像网络基准的竞争力,且有最少的铃声和哨声。