Improving Generalization via Scalable Neighborhood Component Analysis

Current visual recognition is dominated by the end-to-end formulation of classification problems implemented by the parametric softmax classifiers. Such formulation makes a closed world assumption with a fixed set of categories. This becomes problematic for open-set scenarios where new categories are encountered with very few examples for learning a generalizable parametric classifier. This paper adopts a non-parametric approach for visual recognition by optimizing feature embeddings instead of parametric classifiers. We use a deep neural network to learn embeddings which preserves neighborhood structures by neighborhood component analysis (NCA). Limited by its computational bottlenecks, we devise a mechanism to use an augmented memory to scale NCA for large datasets and very deep neural networks. Our experimental results show state-of-the-art results on ImageNet classification using nearest neighbor classifiers. More importantly, our feature embedding is more generalizable for new categories such as sub-category discovery and few-shot recognition.