Few-Shot Learning with Metric-Agnostic Conditional Embeddings
Nathan Hilliard Lawrence Phillips Scott Howland Artëm Yankov Courtney D. Corley Nathan O. Hodas
Abstract
Learning high quality class representations from few examples is a key problem in metric-learning approaches to few-shot learning. To accomplish this, we introduce a novel architecture where class representations are conditioned for each few-shot trial based on a target image. We also deviate from traditional metric-learning approaches by training a network to perform comparisons between classes rather than relying on a static metric comparison. This allows the network to decide what aspects of each class are important for the comparison at hand. We find that this flexible architecture works well in practice, achieving state-of-the-art performance on the Caltech-UCSD birds fine-grained classification task.
中文速览
少样本学习（few-shot learning）的核心难题在于如何从极少量图片中为每个新类别学出高质量的表示，现有度量学习方法普遍依赖固定的欧氏距离或余弦距离来做类别比较，灵活性不足。为此，作者提出了一种名为MACO（Metric-Agnostic Conditional network）的新架构，它用关系网络对同类图片做成对比较来生成类别向量，再让查询图片去"条件化"地调整每个类别的表示——相当于让模型根据当前要判断的图片动态决定哪些类别特征最值得关注——最后用一个可学习的神经网络代替固定距离度量来完成分类。在细粒度鸟类识别基准CUB上，MACO达到了当时最优水平，且消融实验证明条件化表示和去掉固定度量这两个设计缺一不可。这项工作说明少样本分类不必依赖预设的度量空间，让类别表示随查询动态调整才是提升性能的关键，对理解人类快速学习新概念的机制也提供了有趣的计算视角。
原文 arXiv:1802.04376;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1802.04376v1