Adversarially-Trained Deep Nets Transfer Better

11 Jul 2020Francisco UtreraEvan KravitzN. Benjamin ErichsonRajiv KhannaMichael W. Mahoney

Transfer learning has emerged as a powerful methodology for adapting pre-trained deep neural networks to new domains. This process consists of taking a neural network pre-trained on a large feature-rich source dataset, freezing the early layers that encode essential generic image properties, and then fine-tuning the last few layers in order to capture specific information related to the target situation... (read more)

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