Fine-tuning through knowledge transfer from a pre-trained model on a
large-scale dataset is a widely spread approach to effectively build models on
small-scale datasets. In this work, we show that a recent adversarial attack
designed for transfer learning via re-training the last linear layer can
successfully deceive models trained with transfer learning via end-to-end
fine-tuning. This raises security concerns for many industrial applications. In
contrast, models trained with random initialization without transfer are much
more robust to such attacks, although these models often exhibit much lower
accuracy. To this end, we propose noisy feature distillation, a new transfer
learning method that trains a network from random initialization while
achieving clean-data performance competitive with fine-tuning. Code available
at https://github.com/cmu-enyac/Renofeation.

360 Mobile Vision - 360mobilevision.com North & South Carolina Security products and Systems Installations for Commercial and Residential - $55 Hourly Rate. ACCESS CONTROL, INTRUSION ALARM, ACCESS CONTROLLED GATES, INTERCOMS AND CCTV INSTALL OR REPAIR 360 Mobile Vision - 360mobilevision.com is committed to excellence in every aspect of our business. We uphold a standard of integrity bound by fairness, honesty and personal responsibility. Our distinction is the quality of service we bring to our customers. Accurate knowledge of our trade combined with ability is what makes us true professionals. Above all, we are watchful of our customers interests, and make their concerns the basis of our business.

By admin