判别式
杠杆(统计)
人工智能
计算机科学
边距(机器学习)
模式识别(心理学)
相似性(几何)
机器学习
集合(抽象数据类型)
无监督学习
语义学(计算机科学)
分类
监督学习
图像(数学)
人工神经网络
程序设计语言
作者
Wentao Chen,Zhang Zhang,Wei Wang,Liang Wang,Zilei Wang,Tieniu Tan
标识
DOI:10.1016/j.patcog.2022.108986
摘要
Few-shot learning aims to recognize novel concepts with only a few examples. To this end, previous studies resort to acquiring a strong inductive bias via meta-learning on a group of similar tasks, which however needs a large labeled base dataset to sample training tasks. In this paper, we show that such inductive bias can be learned from a flat collection of unlabeled images, and instantiated as transferable representations among seen and unseen classes. Specifically, we propose a novel unsupervised Part Discovery Network (PDN) to learn transferable representations from unlabeled images, which automatically selects the most discriminative part from an input image and then maximizes its similarities to the global view of the input and other neighbors with similar semantics. To better leverage the learned representations for few-shot learning, we further propose Part-Aligned Similarity (PAS), the key of which is to measure image similarities based on a set of discriminative and aligned parts. We conduct extensive studies on five popular few-shot learning datasets to evaluate our approach. The experimental results show that our approach outperforms previous unsupervised methods by a large margin and is even comparable with state-of-the-art supervised methods.
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