DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
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.
DeCAF has no earlier papers in this dataset.
Led to
- ZFNet2013 · cited 2דThe generalization ability of convnet features is also explored in concurrent work by (Donahue et al. 2013).”From ZFNet · §Introduction
- CNN features off-the-shelf2014 · cited 4דThe experiments confirm and extend the results reported in Donahue14.”From CNN features off-the-shelf · §Conclusion
- ResNeXt2016 · cited 2דIn contrast to traditional hand-designed features (e.g., SIFT Lowe2004 and HOG Dalal2005), features learned by neural networks from large-scale data Russakovsky2015 require minimal human involvement during training, and…”From ResNeXt · §Introduction
- Do better ImageNet models transfer bette2018 · cited 5דNetwork architectures measured against this dataset have fueled much progress in computer vision research across a broad array of problems, including transferring to new datasets donahue2014decaf; razavian2014cnn, object…”From Do better ImageNet models transfer bette · §Introduction
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.