DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Title | DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition |
Publication Type | Conference Paper |
Year of Publication | 2014 |
Authors | Donahue, J., Jia Y., Vinyals O., Hoffman J., Zhang N., Tzeng E., & Darrell T. |
Other Numbers | 3631 |
Abstract | We evaluate whether features extracted fromthe activation of a deep convolutional networktrained in a fully supervised fashion on a large,fixed set of object recognition tasks can be re-purposed to novel generic tasks. Our generictasks may differ significantly from the originallytrained tasks and there may be insufficient labeled or unlabeled data to conventionally train oradapt a deep architecture to the new tasks. We investigate and visualize the semantic clustering ofdeep convolutional features with respect to a variety of such tasks, including scene recognition,domain adaptation, and fine-grained recognitionchallenges. We compare the efficacy of relyingon various network levels to define a fixed feature, and report novel results that significantlyoutperform the state-of-the-art on several important vision challenges. We are releasing DeCAF,an open-source implementation of these deepconvolutional activation features, along with allassociated network parameters to enable visionresearchers to be able to conduct experimentation with deep representations across a range ofvisual concept learning paradigms. |
Bibliographic Notes | Proceedings of the 31st International Conference in Machine Learning (ICML), Beijing, China |
Abbreviated Authors | J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell |
ICSI Research Group | Vision |
ICSI Publication Type | Article in conference proceedings |