Implementation And Performance Analysis Of Few-shot Image Classification And Generation

Deep learning has been a great success in the field of computer vision,including widely used in fields such as medical imaging,identification,intelligent transportation,image synthesis,with the rapid development of deep learning algorithms,artificial intelligence technology is gradually entering the society widely,becoming an important technology related to national economy and people's livelihood.However,for deep learning models,better experimental results usually rely on massive data set resources and long-term training,then harsh hardware resource conditions and high-quality data sets are usually necessary.On the one hand,this will consume a lot of manpower and financial resources,on the other hand,it is difficult to collect such a large amount of data in some situations in reality.Data scarcity often becomes a bottleneck for deep learning,which undoubtedly limits the development and practicality of deep learning technology Application,so it has important theoretical significance and application value for the study of few-shot learning.The current deep neural network algorithm also lacks interpretability,and the generalization ability of this algorithm still have a lot of room for improvement.In recent years,image generation technology has also received extensive attention.It has achieved remarkable success in face generation,image super-resolution,image style transfer,and image restoration.Image generation technology also helps to better model sample data.Complement each other with image recognition technology,and can further improve the accuracy of the recognition model by expanding the data set,the image generation technology also has some difficulties in the stability of training and the quality and diversity of the results.Therefore,it is important to study the image generation technology under few-shot conditions and further improve the performance of the few-shot image classification model.The development of deep learning technology in recent years has largely benefited from the advancement of hardware.In addition to the field of computer vision,deep learning is also widely used in speech recognition,advertising recommendation,search engines,games,finance,etc.With the development of big data and artificial intelligence technology,more and more applications in these fields can be solved through deep learning technology.As a safety-related system,autonomous driving is also an important research direction of artificial intelligence.Currently,5G network technology is accelerating deployment.It is a general trend for technology to shift from the server side to the embedded terminal more.In these situations,the requirements for the accuracy and real-time of the system are becoming higher and higher.The hardware technology also plays a more important role.Therefore,it is of great practical significance to analyze and summarize those artificial intelligence new hardware.This paper firstly researches the few-shot learning technology and the capsule routing theories,and introduces the principles of the current mainstream few-shot image recognition algorithm and capsule algorithm.Combining the dynamic capsule routing iterative consistency mechanism with the prototype network,then design a few-shot classification model that is more robust to the sample new pose in the test,in view of the defects in it,a multi-margin cosine loss function is proposed for the model,and finally a CosGCN model is proposed,which ensures the recognition accuracy and improves the calculation efficiency during training process,experiments were conducted on the few-shot datasets Omniglot and miniImagenet,the results show that our model has better posture robustness.Then the current mainstream image generation algorithms were studied,including GANs and VAEs,the improved algorithms and hybrid models of these methods,and then adjusts the training strategy and loss function based on the IntroVAE model.A model suitable for few-shot image generation is proposed.The training model on the Omniglot dataset can generate sharp and diversity results,and the recognition accuracy of the proposed image classification model is improved by expanding the dataset.This paper also analyzes and summarizes the hardware currently used in artificial intelligence,including the GPU of the PC platform,embedded GPU,neural network accelerator,and FPGA.The algorithm proposed in this paper is implemented on the GPU of the PC platform,the calculation of hardware resources and the computing efficiency is analyzed,and finally looks forward to the future research direction that can be applied on the embedded platform through the lightweight design of the model.