## 1 Introduction  
Vehicle ReID [1] attempts to locate the identity of a specific vehicle in a huge network of cameras quickly. It has been used in a variety of contexts. For starters, vehicle ReID [2] can help police fight crime. Moreover, it can aid city planners in gaining a better understanding of traffic flow. With a strong application background, vehicle ReID is gaining traction in computer vision tasks. Over the last decade, deep learning has become popular and a major method used for computer vision. The research community has been driven to create CNN-based [3] approaches for vehicle ReID tasks. In order to train a model with good performance throughout the training process, neural networks typically require quite massive amount of data. Data collection and annotation, on the other hand, are quite costly. Moreover, a shortage of data is an obvious stumbling point when building a strong deep neural network. It frequently results in over-fitting or poor generalization. Data limitation leads to performance reduction in the field of vehicle ReID as well. Data augmentation [4] is a good method for obtaining extra training samples without having to collect and annotate more data. Common augmentation methods typically generate more training samples by data warping. Moreover, data warping includes random erasing [5], adversarial training [6], geometric and color transformations, and neural style transfer [7]. Unlike previous traditional methods [8], we propose a novel data augmentation strategy that increases the complexity rather than the number of samples in a dataset. In particular, our method blends traditional augmentation method with deep learning technology. A general framework has been attempted in our previous work [9] to exercise this idea. To make the framework more efficient, we adopt the architecture and reorder some of the modules. We introduce a filter matrix to blur the local region of an image sample in order to increase the difficulty of network learning. First, we use a local region selection and a light weight neural network to learn the filter matrix for further data augmentation. Here, we define this light weight neural network as the parameter generator network (PGN). The special purpose of this step is to retain essential information while increasing the difficulty of identification. Second, an adversarial module is employed in our framework. This module includes a recognizer and a parameter generator, as previously mentioned. The recognizer is intended for identifying the vehicles in datasets that belong to the same identity. The parameter generator (PGN), on the other hand, generates augmented image samples. However, the identities of these augmented images are not necessarily the same as the identities of the original images. Thus, the recognizer and PGN form an adversarial module. The two modules compete with each other and strike a balance between the consistency of vehicle identities and the variation of transformed samples. The results of augmented images are depicted in Figure 1. Extensive experiments prove that our framework has excellent generalizability. For validation, three datasets, i.e., VeRi-776 [10], VehicleID [11], and VERI-Wild [12], are subjected to a series of asymptotic ablation experiments. Our experiments show that the framework proposed greatly surpass the baseline and other previous methods. Our contributions are summed up as follows:  
1. A data augmentation method that combines traditional data augmentation and deep learning technology;  
Figure 1: Image augmentation results of our proposed method. A deep learning framework that combines region location selection, local blur transformation, and an adversarial framework;
* Rather than adding more samples, the dataset is expanded by making the data samples harder. The original structure of the dataset is retained;
* The proposed framework optimizes both the data augmentation and the recognition model without any fine-tuning. The augmented samples are created by dynamic learning.