Abstract
Anomaly detection refers to the process of detecting anomalies from data that do not follow its distribution. In recent years, Transformer-based methods utilizing generative adversarial networks (GANs) have shown remarkable performance in this field. Unlike traditional convolutional architectures, Transformer structures have advantages in capturing long-range dependencies, leading to a substantial improvement in detection performance. However, transformer-based models may be limited in capturing fine-grained details as well as the inference speed. In this paper, we propose a scalable convolutional Generative Adversarial Network (GAN) called GanNeXt. Our design incorporates a new convolutional architecture that utilizes depthwise convolutional layers and pointwise convolutional layers as extension layers. In addition, we introduce skip connections to capture multi-scale local details. Experiments demonstrate that our proposed method achieves a 58% reduction in floating-point operations per second (FLOPs), while outperforming state-of-the-art Transformer-based GAN baselines on CIFAR10 and STL10 datasets. The codes will be available at https://github.com/SYLan2019/GanNeXt.