Machine Learning and Intelligent Optimization Algorithms: Principles of DLSS and Cross-Industry Applications
Main Article Content
Keywords
machine learning, neural networks, intelligent optimization algorithms, DLSS technology, cross-industry applications, real-time high-resolution processing
Abstract
Machine learning and artificial intelligence optimization algorithms are core supporting technologies of artificial intelligence and are widely applied across various scenarios. However, they face challenges such as insufficient generalization capability and low computational efficiency. In high-resolution real-time processing, the “balance between accuracy and efficiency” has become a common bottleneck across industries, which traditional methods struggle to address. Taking NVIDIA DLSS super-resolution and multi-frame generation technology as a case study, this paper systematically reviews its machine learning paradigms, mathematical foundations, and core principles of neural network architecture. It analyzes the universality of intelligent optimization strategies and their application value in non-entertainment fields. The study reveals a general technical pathway of “multimodal feature fusion – temporal information reuse – multi-objective optimization” and demonstrates its empowerment mechanism for high-resolution real-time processing across industries. The algorithmic principles behind DLSS exhibit strong cross-industry transferability and can provide efficient solutions for medical imaging, autonomous driving, remote sensing monitoring, and other fields, thereby promoting technological upgrading across multiple industries.
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