Reducing overfitting in vehicle recognition by decorrelated sparse representation regularisation

Abstract

Most state-of-the-art vehicle recognition methods benefit from the excellent feature extraction capabilities of convolutional neural networks (CNNs), which allow the models to perform well on the intra-dataset. However, they often show poor generalisation when facing cross-datasets due to the overfitting problem. For this issue, numerous studies have shown that models do not generalise well in new scenarios due to the high correlation between the representations in CNNs. Furthermore, over-parameterised CNNs have a large number of redundant representations. Therefore, we propose a novel Decorrelated Sparse Representation (DSR) regularisation. (1) It tries to minimise the correlation between feature maps to obtain decorrelated representations. (2) It forces the convolution kernels to extract meaningful features by allowing the sparse kernels to have additional optimisation. The DSR regularisation encourages diverse representations to reduce overfitting. Meanwhile, DSR can be applied to a wide range of vehicle recognition methods based on CNNs, and it does not require additional computation in the testing phase. In the experiments, DSR performs better than the original model on the intra-dataset and cross-dataset. Through ablation analysis, we find that DSR can drive the model to focus on the essential differences among all kinds of vehicles.