# Improved Video Anomaly Detection with Dual Generators and Channel Attention
## 2 Related Work  
The methods for video anomaly detection have evolved from traditional statistical inference techniques to cutting-edge deep learning models, including reconstruction and prediction approaches. These deep learning-based methods enhance both the quality of video frame feature extraction and the accuracy of detection [3; 4; 5; 6]. The widely used techniques include autoencoders, generative adversarial networks, and attention mechanisms.  
The autoencoder network is designed to uncover the hidden features of input data and obtain more effective features. The network comprises an encoder and a decoder. The encoder reduces the dimensionality of the input video frame and extracts the global latent features associated with it. The decoder then uses the learned Gaussian distribution to upscale the extracted latent features, producing a map that resembles the original video frame. Autoencoders are typically successful in accurately reconstructing anomalies, even when trained on normal data alone. However, this capability can sometimes lead to a decline in detection performance [7; 8]. To this end, Reference [9] proposes spatial rotation transformation and temporal mixing transformation to avoid the generative ability of the model to even predict anomalies. References [10; 11] propose to employ a memory mechanism on the latent space between the encoder and decoder of an autoencoder to limit the reconstruction ability in the case of abnormal inputs. Similarly, [PERSON] [12] proposes to train an unsupervised object-centric convolutional autoencoder to extract feature vectors, divide the training samples into different clusters, use SVM to divide different clusters, and perform anomaly scoring for the classification.  
Generative adversarial networks (GANs) [13] consist of two key components: generators and discriminators. These elements engage in an adversarial learning process where the generator produces fake data samples and tries to trick the discriminator, while the discriminator attempts to identify the generated samples as different from the actual data. Through repeated cycles of competition, both the generator and discriminator continually improve their abilities [14]. For instance, [PERSON] [15] proposes a context-related video anomaly detection method combined with a two-branch generative adversarial network. Reference [16] presents a generative adversarial network with a dual discriminator to predict future frames.  
The amount of information a model can store, as well as its expressive power, grows with the parameters. However, the large number of parameters can lead to information overload. The attention mechanism [17] enables the model to focus on the information that is more critical to the current task, alleviating the problem of information overload and model inefficiency [18; 19; 20]. To make the extractor pay more attention to abnormal regions during feature extraction, [PERSON] [21] proposes an extractor augmented by a self-guided attention module. Reference [22] proposes a residual attention-based autoencoder for video anomaly detection.  
In this paper, we present a novel approach to video anomaly detection that leverages the power of GAN and integrates attention mechanisms and autoencoders. Concretely, our method is built on the general framework of GANs and employs two generators to produce pseudo frames and reconstructed frames. The generator incorporates autoencoders to ensure stable training, and the attention mechanism is embedded to enhance the interdependence among the video frames and to concentrate on vital feature information. The details of our work will be described in detail in Section 3.