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About Learning Lightweight Lane Detection Cnns by Self Attention Distillation
This document summarizes a research paper that proposes a novel knowledge distillation approach called Self Attention Distillation (SAD) for training lightweight lane detection models. SAD allows a model to learn from its own attention maps without additional supervision or labels. Specifically, attention maps from deeper layers that encode rich context are used as distillation targets to refine representations learned in shallower layers. The authors validate SAD on several lane detection benchmarks, finding that it significantly improves performance of lightweight models like ENet with minimal overhead.
Detailed Information
| Author: | ['AndroidRumpers'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 10 |
| Language: | English |
| Format: | |
| Price: | FREE |
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