A Deep Neural Network Based Transfer Learning Method

An important hypothesis for many machine learning and data mining algorithms is that training data and test data are in the same feature space and have the same distribution.However,in many real-world applications,this assumption may not be true.For example,if you want to classify data in a certain domain A,but the amount of data in the domain is not enough,at the same time,there is enough data in another similar domain B to train the model,but the data in domain B may be in a different feature space or disobey the same distribution with the data in domain A.At this time,successful knowledge transfer will greatly improve machine learning performance and reduce the cost of ma nually annotating data.In recent years,transfers learning has emerged as a new learning framework.Deep learning is a method of characterizing learning based on data in machine learning.It is a new field in machine learning.Its motivation lies in building and simulating the neural network of the human brain to analyze and learn.It imitates the mechanism of the human brain.To interpret data such as images,sounds,and text.Since Alex Net won the championship in the Image Net competition in 2012,deep learning has achieved great success in the field of computer vision research and has attracted the attention of many researchers.However,deep learning requires a large amount of training data.Obtaining these training data will consume a lot of manpower an d material resources.If a new model is to be trained in a new similar field,discarding these large amounts of training data under different distributions will be very wasteful.of.Therefore,the combination of deep learning and transfer learning techniq ues will improve training accuracy while improving model accuracy.This paper will mainly introduce twodeep transfer learning classification methods,and compare their advantages and disadvantages.Specific mo dels include the following two:(1)Deep adaptation network.This kind of network will first process the data in two domains through deep network processing,and then map the output of some layers of the network into a certain feature space.By reducing the classification error rate of the classifier i n the source domain and the distance between the data in the source domain and the target domain in the feature space,the purpose of improving the classification per formance in the target domain can be achieved.(2)Deep adversarial network.The Generative adversarial network has brought a new wave in the field of deep learning.The con volutional layer of the deep network can extract the feature of the data,and then the full-connected layer classifies these features,deep adversarial network utilizes this idea,and adds a domain classifier after the convolutional layer.Assuming that the domain classifier can correctly classify the data domain,then only the convolution layer needs to be trained so that the extracted features cannot be correctly classifie d by the domain classifier,and the domain-invariant features between the domains can be obtained.Using this kind of domain-invariant features can also achieve the purpose of transfer learning.