In the previous article, we talked about some problems in the training of deep learning Xi Xi models in the industrial field. In today's article, let's take a look at what solutions are there to these existing problems
Defect data generation: Artificial intelligence technology is used to automatically complete the generation of defect** data, so as to solve the problem of lack of defect samples.
Reduce sample data dependence: From small sample Xi learning, migration Xi, and anomaly detection, reducing the number of defective samples.
Data management: Through digital technology, the systematic control of multi-station and multi-scenario data is realized, and the impact of human factors on data management and control is reduced.
Large-scale datasets: Large-scale datasets and computing resources are used to train deep Xi models, so as to improve the generalization, robustness, and scenario adaptability of the models.
Data annotation: Select the samples that are most helpful to the model Xi for annotation, reduce the workload of manual annotation, and improve the annotation efficiency.
Multimodal data fusion: Multimodal feature fusion, feature fusion based on image data flow and other aspects of technical exploration to improve the generalization performance of the model.