Discuss the concept of transfer learning and its application in artificial intelligence

In the traditional artificial intelligence training process, machine learning plays a leading role. It is trained on data provided by the developer and outputs results that match the data provided. However, the emergence of transfer learning enables artificial intelligence to have the ability to draw inferences from one instance and to draw inferences about other cases, and is no longer limited to a fixed knowledge structure

At present, there are two main methods of transfer learning: instance weighting method and common feature learning method. The instance weighting method refers to increasing the number of learning samples in a certain field and increasing the weight proportion to exercise the machine's preference when outputting results. The common feature learning method is to mark metadata with common features to create correlation between the two, thereby expanding the logical channel

The development of transfer learning has gone through several stages. At first, people tried to analyze the content and structure, so that the machine can find commonalities in different types of problems when learning, instead of being limited to finding output results in a single field. In order to achieve this goal, the field of vision attempts to layer problems according to certain commonalities to facilitate machine learning. If there is new content that needs to be added, the remaining content will be fixed and only a certain level of data will be used for training to eliminate other interference.

When migrating from one domain to another, if the migration is from more data to less data, it is called a single-step migration. Usually, you can build a deep architecture and add as many different levels of problems as possible to logically lose

In data generative transfer learning, the generative adversarial network can be allowed to grow together, and simulated data can be used to stimulate both parties' understanding of commonalities, thereby promoting the growth of the model. This method requires a smaller amount of data and can improve the overall learning efficiency

The enthusiasm for learning artificial intelligence in society has reached a new climax; and the application of transfer learning can undoubtedly make the operation of artificial intelligence more humanistic and enable it to have a wider range of application scenarios.

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