  Obtaining compact and discriminative features is one of the major challenges
in many of the real-world image classification tasks such as face verification
and object recognition. One possible approach is to represent input image on
the basis of high-level features that carry semantic meaning which humans can
understand. In this paper, a model coined deep attribute network (DAN) is
proposed to address this issue. For an input image, the model outputs the
attributes of the input image without performing any classification. The
efficacy of the proposed model is evaluated on unconstrained face verification
and real-world object recognition tasks using the LFW and the a-PASCAL
datasets. We demonstrate the potential of deep learning for attribute-based
classification by showing comparable results with existing state-of-the-art
results. Once properly trained, the DAN is fast and does away with calculating
low-level features which are maybe unreliable and computationally expensive.
