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Deep convolutional neural networks provide a powerful feature learningcapability for image classification. The deep image features can be utilized todeal with many image understanding tasks like image classification and objectrecognition. However, the robustness obtained in one dataset can be hardlyreproduced in the other domain, which leads to inefficient models far fromstate-of-the-art. We propose a deep collaborative weight-based classification(DeepCWC) method to resolve this problem, by providing a novel option to fullytake advantage of deep features in classic machine learning. It firstlyperforms the L2-norm based collaborative representation on the original images,as well as the deep features extracted by deep CNN models. Then, two distancevectors, obtained based on the pair of linear representations, are fusedtogether via a novel collaborative weight. This collaborative weight enablesdeep and classic representations to weigh each other. We observed thecomplementarity between two representations in a series of experiments on 10facial and object datasets. The proposed DeepCWC produces very promisingclassification results, and outperforms many other benchmark methods,especially the ones claimed for Fashion-MNIST. The code is going to bepublished in our public repository.

Collaboratively Weighting Deep and Classic Representation via L2 Regularization for Image Classification