DeCAF is a deep convolutional activation feature designed for generic visual recognition tasks. The paper evaluates whether features extracted from a deep convolutional network trained on a large set of object recognition tasks can be repurposed for new, diverse tasks. The authors investigate the semantic clustering of deep convolutional features across various tasks, including scene recognition, domain adaptation, and fine-grained recognition. They compare the effectiveness of different network levels for defining fixed features and report results that significantly outperform state-of-the-art methods on several vision challenges. DeCAF is an open-source implementation of these features, along with all associated network parameters, to enable researchers to experiment with deep representations across various visual concept learning paradigms. The paper introduces DeCAF as a visual feature derived from a deep convolutional network trained on ImageNet. It demonstrates that DeCAF outperforms conventional visual representations on standard benchmark tasks, including Caltech-101, the Office dataset, the Caltech-UCSD Birds dataset, and the SUN-397 scene recognition database. The authors analyze the semantic salience of deep convolutional features, finding that they cluster semantic topics more effectively than conventional features. They also show that DeCAF's computational efficiency is comparable to existing features like HOG and KDES. The paper also explores the use of DeCAF in domain adaptation, subcategory recognition, and scene recognition tasks. It shows that DeCAF performs well in these tasks, outperforming existing methods. The authors conclude that DeCAF is a powerful feature for general object recognition and that its ability to generalize to different tasks is significant. The paper highlights the importance of multi-task learning and the effectiveness of deep learning in visual recognition tasks. The authors also note that DeCAF can be used as an off-the-shelf visual representation without heavy computation. The paper is supported by extensive experiments and comparisons with existing methods, demonstrating the effectiveness of DeCAF in various visual recognition tasks.DeCAF is a deep convolutional activation feature designed for generic visual recognition tasks. The paper evaluates whether features extracted from a deep convolutional network trained on a large set of object recognition tasks can be repurposed for new, diverse tasks. The authors investigate the semantic clustering of deep convolutional features across various tasks, including scene recognition, domain adaptation, and fine-grained recognition. They compare the effectiveness of different network levels for defining fixed features and report results that significantly outperform state-of-the-art methods on several vision challenges. DeCAF is an open-source implementation of these features, along with all associated network parameters, to enable researchers to experiment with deep representations across various visual concept learning paradigms. The paper introduces DeCAF as a visual feature derived from a deep convolutional network trained on ImageNet. It demonstrates that DeCAF outperforms conventional visual representations on standard benchmark tasks, including Caltech-101, the Office dataset, the Caltech-UCSD Birds dataset, and the SUN-397 scene recognition database. The authors analyze the semantic salience of deep convolutional features, finding that they cluster semantic topics more effectively than conventional features. They also show that DeCAF's computational efficiency is comparable to existing features like HOG and KDES. The paper also explores the use of DeCAF in domain adaptation, subcategory recognition, and scene recognition tasks. It shows that DeCAF performs well in these tasks, outperforming existing methods. The authors conclude that DeCAF is a powerful feature for general object recognition and that its ability to generalize to different tasks is significant. The paper highlights the importance of multi-task learning and the effectiveness of deep learning in visual recognition tasks. The authors also note that DeCAF can be used as an off-the-shelf visual representation without heavy computation. The paper is supported by extensive experiments and comparisons with existing methods, demonstrating the effectiveness of DeCAF in various visual recognition tasks.