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Knowledge transferability, or transfer learning, has been widely adopted to allow a pre-trained model in the source domain to be effectively adapted to downstream tasks in the target domain.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
An analysis of single-layer networks in unsupervised feature learning
Coates, A., Ng, A., and Lee, H · 2011
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2014
Earlier work this paper cites.
How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., and Lipson, H · 2014
Earlier work this paper cites.
Facial landmark detection by deep multi-task learning
Zhang, Z., Luo, P., Loy, C. C., and Tang, X · 2014
Earlier work this paper cites.
Multi-task learning for multiple language translation
Dong, D., Wu, H., He, W., Yu, D., and Wang, H · 2015
Earlier work this paper cites.
Learning transferable features with deep adaptation networks
Long, M., Cao, Y., Wang, J., and Jordan, M · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al · 2015
Earlier work this paper cites.
Character-level convolutional networks for text classification
Zhang, X., Zhao, J., and LeCun, Y · 2015
Earlier work this paper cites.
What makes imagenet good for transfer learning?
Huh, M., Agrawal, P., and Efros, A. A · 2016
Earlier work this paper cites.
Delving into transferable adversarial examples and black-box attacks
Liu, Y., Chen, X., Liu, C., and Song, D · 2016
Earlier work this paper cites.
Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
Papernot, N., McDaniel, P., and Goodfellow, I · 2016
Cited alongside, same era.
Adversarial image perturbation for privacy protection–a game theory perspective
Joon Oh, S., Fritz, M., and Schiele, B · 2017
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2017
Cited alongside, same era.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, A., Carlini, N., and Wagner, D · 2018
Cited alongside, same era.
Black-box adversarial attacks with limited queries and information
Ilyas, A., Engstrom, L., Athalye, A., and Lin, J · 2018
Cited alongside, same era.
Why do adversarial attacks transfer? explaining transferability of evasion and poisoning attacks
Demontis, A., Melis, M., Pintor, M., Jagielski, M., Biggio, B., Oprea, A., Nita-Rotaru, C., and Roli, F · 2019
Later among the works it cites.
Evading defenses to transferable adversarial examples by translation-invariant attacks
Dong, Y., Pang, T., Su, H., and Zhu, J · 2019
Later among the works it cites.
Improving adversarial robustness of ensembles with diversity training
Kariyappa, S. and Qureshi, M. K · 2019
Later among the works it cites.
Cross-domain transferability of adversarial perturbations
Naseer, M. M., Khan, S. H., Khan, M. H., Khan, F. S., and Porikli, F · 2019
Later among the works it cites.
Understanding the effects of pre-training for object detectors via eigenspectrum
Shinya, Y., Simo-Serra, E., and Suzuki, T · 2019
Later among the works it cites.
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Multi-task learning using uncertainty to weigh losses for scene geometry and semantics
Kendall, A., Gal, Y., and Cipolla, R · 2018
Cited alongside, same era.
Characterizing adversarial subspaces using local intrinsic dimensionality
Ma, X., Li, B., Wang, Y., Erfani, S. M., Wijewickrema, S., Schoenebeck, G., Song, D., Houle, M. E., and Bailey, J · 2018
Cited alongside, same era.
Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Miyato, T., Maeda, S.-i., Koyama, M., and Ishii, S · 2018
Cited alongside, same era.
Taskonomy: Disentangling task transfer learning
Zamir, A. R., Sax, A., Shen, W., Guibas, L. J., Malik, J., and Savarese, S · 2018
Cited alongside, same era.
Transferable adversarial perturbations
Zhou, W., Hou, X., Chen, Y., Tang, M., Huang, X., Gan, X., and Yang, Y · 2018
Cited alongside, same era.
Task2vec: Task embedding for meta-learning
Achille, A., Lam, M., Tewari, R., Ravichandran, A., Maji, S., Fowlkes, C. C., Soatto, S., and Perona, P · 2019
Cited alongside, same era.
Ensemble adversarial training: Attacks and defenses
Tramèr, F., Kurakin, A., Papernot, N., Goodfellow, I., Boneh, D., and McDaniel, P
Cited in the paper.
Improving transferability of adversarial examples with input diversity
Xie, C., Zhang, Z., Zhou, Y., Bai, S., Wang, J., Ren, Z., and Yuille, A. L · 2019
Later among the works it cites.
Larger norm more transferable: An adaptive feature norm approach for unsupervised domain adaptation
Xu, R., Li, G., Yang, J., and Lin, L · 2019
Later among the works it cites.
Do adversarially robust imagenet models transfer better?
Salman, H., Ilyas, A., Engstrom, L., Kapoor, A., and Madry, A · 2020
Closest in time.
Adversarially-trained deep nets transfer better
Utrera, F., Kravitz, E., Erichson, N. B., Khanna, R., and Mahoney, M. W · 2020
Closest in time.
T3: Tree-autoencoder constrained adversarial text generation for targeted attack
Wang, B., Pei, H., Pan, B., Chen, Q., Wang, S., and Li, B · 2020
Closest in time.