Regressing CNN image features to random pseudo-targets during fine-tuning acts as an effective regularizer, improving transfer learning accuracy on par with concrete auxiliary-task methods.
Borrowing treasures from the wealthy: Deep transfer learning through selective joint fine-tuning
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Regularizing CNN Transfer Learning with Randomised Regression
Regressing CNN image features to random pseudo-targets during fine-tuning acts as an effective regularizer, improving transfer learning accuracy on par with concrete auxiliary-task methods.