The approach generates transferable examples to fill in the gap between the source and target domains, and adversarially trains the deep classifiers to make consistent predictions over the transferable examples.
To this end, we propose Transferable Adversarial Training (TAT) to enable the adaptation of deep classifiers. The approach generates transferable examples to ...
Aug 10, 2020 · TAT [20] proposes transferable adversarial training to guarantee the adaptability. BSP [5] balances between the transferability and ...
Transferable Adversarial Training (TAT) is proposed to enable the adaptation of deep classifiers and advances the state of the arts on a variety of domain ...
Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers (ICML 2019) · Dataset · Requirements · Usage · Citation · Reference codes · Contact.
Jun 8, 2019 · Minimize the source risk. Train the model with supervision from the source domain. Minimize the discrepancy term.
Transferable adversarial training: A general approach to adapting deep classi- fiers. In Proceedings of the International Conference on Machine. Learning ...
Transferable adversarial training: A general approach to adapting deep classifiers ... Towards Understanding the Transferability of Deep Representations. H Liu, M ...
Transferable adversarial training: A general approach to adapting deep classifiers ... Towards Understanding the Transferability of Deep Representations. H Liu, M ...
Nov 26, 2021 · Trans- ferable adversarial training: A general approach to adapting deep classifiers. ... Learning transferable features with deep adaptation ...