DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

6 Oct 2013Jeff Donahue • Yangqing Jia • Oriol Vinyals • Judy Hoffman • Ning Zhang • Eric Tzeng • Trevor Darrell

We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be re-purposed to novel generic tasks. Our generic tasks may differ significantly from the originally trained tasks and there may be insufficient labeled or unlabeled data to conventionally train or adapt a deep architecture to the new tasks. We investigate and visualize the semantic clustering of deep convolutional features with respect to a variety of such tasks, including scene recognition, domain adaptation, and fine-grained recognition challenges.

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