TAM: Task-Aware Memory Distillation for Efficient Spatiotemporal Prediction

arXiv:2610.11617 · cs.CV, cs.AI · Submitted 2026-10-08 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-10-08

Updated: 2026-10-08

Terminology

• Distillation — 54× in this paper · explained in 2 episodes
• Knowledge Distillation — 11× in this paper · explained in 11 episodes
• Adam — 4× in this paper · explained in 2 episodes
• Alignment — 4× in this paper · explained in 1 episode
• Benchmark — 4× in this paper · explained in 2 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Embeddings — 2× in this paper · explained in 2 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 4 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Non-stationarity — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes

Sources

• Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control
• Distilling the Knowledge in a Neural Network
• FitNets: Hints for Thin Deep Nets
• A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay
• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

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