System and method for revising samples for neural network training

Abstract

The invention relates to a system and a method for revising samples for neural network training. The system comprises a service terminal and a client front end; the service terminal is set to store samples, distribute the samples to the client front end, receive and store a processing result of the client front end, and generate statistical display according to the processing result; and the client front end is configured to receive the sample, execute revision processing and transmit a processing result to the service terminal. The service terminal comprises a storage module and a statisticsmodule; the client front end comprises an annotation module and an auditing module; the annotation module comprises a preprocessing unit and a fine processing unit; the auditing module can also be setto score the revision quality. According to the invention, a plurality of client front ends can revise samples in the same data set at the same time, so the revising progress of the samples is accelerated, and the time cost is saved; an automatic pretreatment unit is arranged, so that the workload of subsequent fine treatment is reduced; and an auditing and scoring mechanism is set, so that the enthusiasm of the annotator is not struck, and the reliability of the data set sample is improved.

Description

technical field [0001] The invention relates to the field of neural networks, in particular to a system and method for revising samples for neural network training. Background technique [0002] Neural network is currently the fastest-growing and most popular research field in the field of artificial intelligence. It has been widely used in search technology, data mining, machine learning, machine translation, natural language processing and personalized recommendation. In fact, it is a complex machine learning algorithm. By learning the internal laws and expression levels of massive sample data, it enables machines to have the ability to analyze and learn like humans, and to recognize data such as text, images, and sounds. With the expansion of the actual application range of artificial intelligence and the deepening of application scenarios, the requirements for the prediction accuracy of deep learning models are getting higher and higher. Change accordingly. [0003] As...

AI Technical Summary

Problems solved by technology

Collecting and labeling such a large amount of effective data requires extremely high time and money costs. As mentioned above, the needs are constantly changing. To meet new needs, new training data is required, which must be collected every time. With labeled samples, the cost is clearly unacceptable

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