Research On Hybrid Generative Adversarial Networks For Image Recognitio

Deep learning has made significant progress in the field of image classification.Network models such as Alex-Net(Alex Krizhevsky Network),Google-Net(Google Network),and VGG-Net(Visual Geometry Group Network)have been widely used in image classification tasks and have achieved good results.These models can learn discriminative features and recognize clear and complete images.However,in practical applications,it is common to encounter some damaged images,such as scratches,wear,text or graphics overlap,and so on.In addition,due to the prevalence of COVID-19(Coronavirus Disease2019),Masks have become essential protective equipment.However,for scenarios that require facial recognition,Masks can lead to a decrease in accuracy.Therefore,researchers have begun to explore how to improve the accuracy of face recognition under the condition of wearing Masks.This paper proposes "image completion," also known as "image in-painting," an active computer vision research problem that aims to automatically fill in missing parts of corrupted or incomplete images.In this paper,I address this problem not only by using publicly available visual data but also by incorporating of multiple image semantics via generative models.Recent deep learning-based methods have shown promise in the difficult task of in-painting large missing regions in images.These methods can produce visually plausible results in image structures and textures,but they frequently produce distorted structures or blurry textures that are inconsistent with neighboring regions.This is primarily due to convolutional neural networks’ inability to explicitly borrow or copy information from distant spatial locations.Traditional texture and Patch synthesis methods,on the other hand,are particularly well suited for textures that must be borrowed from nearby regions.Inspired by these observations,this paper proposes the concept of model optimization by mixing multiple generative adversarial networks(GANs)together,called P2 P Encoder GAN(Pixel to Pixel Encoder Generative Adversarial Network).This approach not only generates novel image structures,but also explicitly utilizes surrounding image features as references to improve network training and prediction.The model consists of a feed-forward contextual encoder,where a convolutional neural network is trained to generate the content of any image region based on its surrounding environment and conditioned adversarial networks.The generator takes a conditioned image as input and feeds it into the discriminator as well.These networks learn to map input images to output images from a mixed loss function of multiple GANs.Experimental results on the ORL(Olivetti Research Laboratory)face database show that this approach not only achieves higher quality Patching results than existing methods,but also obtains higher recognition scores.This demonstrates that the P2 P Encoder GAN model is an effective image restoration method with broad application prospects in practical scenarios.