Improving Sample Efficiency with Normalized RBF Kernels
This addresses the need for learning more with less data in deep learning, but it is incremental as it builds on existing kernel methods for classification tasks.
The paper tackled the problem of improving sample efficiency in deep learning by using normalized RBF kernels as output layers, resulting in networks that achieve higher sample efficiency, compactness, and separability compared to SoftMax layers on CIFAR-10 and CIFAR-100 datasets.
In deep learning models, learning more with less data is becoming more important. This paper explores how neural networks with normalized Radial Basis Function (RBF) kernels can be trained to achieve better sample efficiency. Moreover, we show how this kind of output layer can find embedding spaces where the classes are compact and well-separated. In order to achieve this, we propose a two-phase method to train those type of neural networks on classification tasks. Experiments on CIFAR-10 and CIFAR-100 show that networks with normalized kernels as output layer can achieve higher sample efficiency, high compactness and well-separability through the presented method in comparison to networks with SoftMax output layer.