# Deep Metric Learning and Image Classification with Nearest Neighbour Gaussian Kernels

## TL;DR

This paper introduces a Gaussian kernel loss function for deep neural networks that enhances both metric learning and image classification by creating well-structured embedding spaces, scalable through approximate nearest neighbor search.

## Contribution

It proposes a novel Gaussian kernel loss and training algorithm that unifies metric learning and classification, improving scalability and embedding quality.

## Key findings

- Outperforms state-of-the-art deep metric learning methods.
- Achieves better classification accuracy than traditional softmax.
- Creates semantically meaningful embedding spaces.

## Abstract

We present a Gaussian kernel loss function and training algorithm for convolutional neural networks that can be directly applied to both distance metric learning and image classification problems. Our method treats all training features from a deep neural network as Gaussian kernel centres and computes loss by summing the influence of a feature's nearby centres in the feature embedding space. Our approach is made scalable by treating it as an approximate nearest neighbour search problem. We show how to make end-to-end learning feasible, resulting in a well formed embedding space, in which semantically related instances are likely to be located near one another, regardless of whether or not the network was trained on those classes. Our approach outperforms state-of-the-art deep metric learning approaches on embedding learning challenges, as well as conventional softmax classification on several datasets.