Neural Interpolation

Neural interpolation focuses on using artificial neural networks to generate continuous outputs from discrete data points, effectively "filling in the gaps" between known values. Current research emphasizes efficient architectures like convolutional neural networks (CNNs) and transformers, often combined to leverage both local and global data relationships, with a focus on reducing computational costs and improving generalization performance. This technique finds applications in diverse fields, from improving weather forecasting resolution and automating cockpit gauge readings to enhancing video frame interpolation and enabling novel approaches to light field rendering.