Search Results for author:
Found 4 papers, 3 papers with code
Cross-domain Cross-architecture Black-box Attacks on Fine-tuned Models with Transferred Evolutionary Strategies
To successfully attack fine-tuned models under both settings, we propose to first train an adversarial generator against the source model, which adopts an encoder-decoder architecture and maps a clean input to an adversarial example.
Two Sides of the Same Coin: White-box and Black-box Attacks for Transfer Learning
To systematically measure the effect of both white-box and black-box attacks, we propose a new metric to evaluate how transferable are the adversarial examples produced by a source model to a target model.
Fisher Deep Domain Adaptation
Though the learned representations are separable in the source domain, they usually have a large variance and samples with different class labels tend to overlap in the target domain, which yields suboptimal adaptation performance.
Parameter Transfer Unit for Deep Neural Networks
Unfortunately, the transferability is usually defined as discrete states and it differs with domains and network architectures.
