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arXiv AI By Yuqi Li, Xiaoqin Feng, Fan Xu, Weilun Feng, Chuanguang Yang, Yingli Tian, Hao Wu

TAM: Task-Aware Memory Distillation for Efficient Spatiotemporal Prediction

Read the original on arXiv AI →

TAM (Task‑Aware Memory Distillation) is a framework that stores a frozen teacher’s knowledge in a bounded, retrievable memory, encoding latent features, forecast changes, or flow residuals. During training, the student matches the teacher’s similarity distribution over shared references or regresses observation‑conditioned residual prototypes, complementing supervised prediction and conventional distillation. Experiments on video prediction, weather forecasting, and traffic flow prediction show that TAM improves SSIM on all six video datasets and reduces MSE on five, achieving up to 1.93% MSE reduction on WeatherBench without affecting student inference cost.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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