Deep learning has enabled various Internet of Things (IoT) applications.
Still, designing models with high accuracy and computational efficiency remains
a significant challenge, especially in real-time video processing applications.
Such applications exhibit high inter- and intra-frame redundancy, allowing
further improvement. This paper proposes a similarity-aware training
methodology that exploits data redundancy in video frames for efficient
processing. Our approach introduces a per-layer regularization that enhances
computation reuse by increasing the similarity of weights during training. We
validate our methodology on two critical real-time applications, lane detection
and scene parsing. We observe an average compression ratio of approximately 50%
and a speedup of \sim 1.5x for different models while maintaining the same
accuracy.

本文提出了一种基于相似度的训练方法，利用视频帧中的数据冗余来实现高效处理，适用于实时视频处理应用。通过引入分层的正则化，提高了计算复用并增加了训练期间权重的相似性，该方法在车道检测和场景分割等关键实时应用中获得了较高的压缩比和加速比，同时保持了相同的准确性。