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Domain Adaptive Transfer Learning with Specialist Models
Jiquan Ngiam, Daiyi Peng, Vijay Vasudevan +3
Transfer learning is a widely used method to build high performing computer vision models. In this paper, we study the efficacy of transfer learning by examining how the choice of…
Lipschitz Networks and Distributional Robustness
Zac Cranko, Simon Kornblith, Zhan Shi +1
Robust risk minimisation has several advantages: it has been studied with regards to improving the generalisation properties of models and robustness to adversarial perturbation. W…
Do Better ImageNet Models Transfer Better?
Simon Kornblith, Jonathon Shlens, Quoc V. Le
Transfer learning is a cornerstone of computer vision, yet little work has been done to evaluate the relationship between architecture and transfer. An implicit hypothesis in moder…