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Full Draft.pdf

1903.01522.pdf

🔗 Source: arxiv.org

📊 Size: 1.79 MB

📄 Pages: 10 pages

⬇️ Downloads: 90

Summary

Deep neural network-based methods achieve outstanding performance on object detection and classification tasks, but require prohibitive runtime and are not suitable for real-time applications. Inspired by the human visual system's ability to adapt to temporal dependencies, a novel framework called Temporal Knowledge Distillation (TKD) is proposed. TKD distills temporal knowledge from a heavy neural network-based model to a light-weight model, using a Long-short Term Memory (LSTM)-based key frame selection method and a novel teacher-bounded loss design. Experimental evaluations on various datasets show consistent improvement in accuracy-speed tradeoffs for object detection, with a throughput of around 220 images per second.

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Description

Deep neural network-based methods achieve outstanding performance on object detection and classification tasks, but require prohibitive runtime and are not...

Technical Information

File Format: PDF
File Size: 1.79 MB
Pages: 10
Language: EN
Total Downloads: 90
Last Updated: 2 hours ago

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