Search Machine Learning Repository: @inproceedings{icml2014c1_donahue14,
    Publisher = {JMLR Workshop and Conference Proceedings},
    Title = {DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition},
    Url = {http://jmlr.org/proceedings/papers/v32/donahue14.pdf},
    Abstract = {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. We compare the efficacy of relying on various network levels to define a fixed feature, and report novel results that significantly outperform the state-of-the-art on several important vision challenges. We are releasing DeCAF, an open-source implementation of these deep convolutional activation features, along with all associated network parameters to enable vision researchers to be able to conduct experimentation with deep representations across a range of visual concept learning paradigms.},
    Author = {Jeff Donahue and Yangqing Jia and Oriol Vinyals and Judy Hoffman and Ning Zhang and Eric Tzeng and Trevor Darrell},
    Editor = {Tony Jebara and Eric P. Xing},
    Year = {2014},
    Booktitle = {Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
    Pages = {647-655}
   }