标题： 新颖性-准备的少样本分类 显示英文标题

标题： Novelty-Prepared Few-Shot Classification

摘要： 少样本分类算法可以通过采用从其他领域丰富数据中预训练的模型来缓解数据稀缺问题，这在许多现实问题中至关重要。 然而，预训练过程通常未考虑到未来对其他概念类别的适应性。 我们发现，一个经典完全训练的特征提取器可能会为未见类别留下很少的嵌入空间，这使得模型难以很好地拟合新类别。 在本工作中，我们提出使用一种新颖准备的损失函数，称为自压缩softmax损失（SSL），用于少样本分类。 SSL可以防止嵌入空间的完全占用。 因此，模型更能准备学习新类别。 在CUB-200-2011和mini-ImageNet数据集上的实验表明，SSL显著提升了最先进性能。 这项工作可能为考虑少样本分类任务的模型容量提供了一些启示。 显示英文摘要

摘要： Few-shot classification algorithms can alleviate the data scarceness issue, which is vital in many real-world problems, by adopting models pre-trained from abundant data in other domains. However, the pre-training process was commonly unaware of the future adaptation to other concept classes. We disclose that a classically fully trained feature extractor can leave little embedding space for unseen classes, which keeps the model from well-fitting the new classes. In this work, we propose to use a novelty-prepared loss function, called self-compacting softmax loss (SSL), for few-shot classification. The SSL can prevent the full occupancy of the embedding space. Thus the model is more prepared to learn new classes. In experiments on CUB-200-2011 and mini-ImageNet datasets, we show that SSL leads to significant improvement of the state-of-the-art performance. This work may shed some light on considering the model capacity for few-shot classification tasks.