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Our\ngeneric tasks may differ significantly from the originally trained tasks and\nthere may be insufficient labeled or unlabeled data to conventionally train or\nadapt a deep architecture to the new tasks. We investigate and visualize the\nsemantic clustering of deep convolutional features with respect to a variety of\nsuch tasks, including scene recognition, domain adaptation, and fine-grained\nrecognition challenges. We compare the efficacy of relying on various network\nlevels to define a fixed feature, and report novel results that significantly\noutperform the state-of-the-art on several important vision challenges. 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