Deep Metric Learning and Image Classification with Nearest Neighbour Gaussian Kernels
This method improves embedding learning for tasks like image classification and metric learning, offering a scalable solution with broad applicability, though it appears incremental as it builds on existing kernel and nearest neighbor techniques.
The paper tackles the problem of distance metric learning and image classification by introducing a Gaussian kernel loss function and training algorithm for convolutional neural networks, which outperforms state-of-the-art deep metric learning approaches and conventional softmax classification on several datasets.
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.