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SAME.pdf
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🔗 Source: youngwei.com
📊 Size: 398 KB
📄 Pages: 16 pages
⬇️ Downloads: 577

Summary

Dynamic transformers, which use multi-exit models to improve efficiency, can be vulnerable to slowdown attacks that reduce their efficiency gain. Existing adversarial attack approaches targeting model accuracy are not effective in slowing down dynamic transformers. A novel slowdown attack framework, SAME, is proposed to reduce the efficiency of multi-exit models by leveraging their design characteristics and utilizing internal predictions to guide adversarial sample generation. Experiments on the GLUE benchmark show that SAME can effectively diminish the efficiency gain of various multi-exit models by 80% on average.

Description

Dynamic transformers, which use multi-exit models to improve efficiency, can be vulnerable to slowdown attacks that reduce their efficiency gain.

Technical Information

  • File Format: PDF
  • File Size: 398 KB
  • Pages: 16
  • Language: EN
  • Total Downloads: 577
  • Last Updated: 2 hours ago

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