Temporal Redundancy

Temporal redundancy, the repetition of information across time in data like videos or sensor streams, is a significant challenge in many machine learning applications, hindering efficiency and scalability. Current research focuses on developing algorithms and model architectures, such as transformers and autoencoders, that effectively reduce this redundancy through techniques like token merging, adaptive inference, and selective attention mechanisms, thereby improving computational speed and memory usage without sacrificing accuracy. This work has significant implications for various fields, including computer vision, video processing, and robotics, enabling the deployment of more efficient and powerful AI systems on resource-constrained devices.