Modular deep learning algorithms for object classifications

Abstract

Artificial intelligence is a kind of technology that simulates human intelligence. It uses computer programs to imitate human thinking and behavior to solve various tasks like classification and regression. AI can be divided into many different subfields, including ML, NLP, CV and reinforcement learning, among others. Among them, machine learning is among the core technologies in the field of AI, which uses algorithms and statistical methods to enable comput ers to automatically learn and improve from data. Modular Progressive learning is a machine learning technique in which a learning task is broken down into multiple subtasks and trained module by module using a series of models, each of which is responsible for solving a different subtask. In the training of each module, the model improves its performance by learning the output of the pre vious module’s model, and outputs it to the next layer’s model for training. Finally, the output of the entire divided model is combined to produce the fi nal result. Modular training techniques can help machine learning models learn complex features and patterns from large amounts of data and achieve supe rior performance in a variety of tasks and problems. Many fields and industries have witnessed the wide utility, including natural language processing, computer vision and speech recognition. However, modular training also has its limita tions, including the need for a large amount of computing resources and time, as well as the sensitivity to the selection of model architecture and hyperpa rameters. Therefore, model design and training strategies need to be carefully considered when using modular training techniques.