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CNNs have made an undeniable impact on computer vision through the ability to learn high-capacity models with large annotated training sets. One of their remarkable properties is the ability to transfer knowledge from a large source dataset to a (typically smaller) target dataset. This is usually accomplished through fine-tuning a fixed-size network on new target data. Indeed, virtually every contemporary...
Recently, sparse representation based classification (SRC) has been successfully used for visual recognition and showed impressive performance. Given a testing sample, SRC computes its sparse linear representation with respect to all the training samples and calculates the residual error for each class of training samples. However, SRC considers the training samples in each class contributing equally...
Recently, dictionary learned by sparse coding has been widely adopted in image classification and has achieved competitive performance. Sparse coding is capable of reducing the reconstruction error in transforming low-level descriptors into compact mid-level features. Nevertheless, dictionary learned by sparse coding does not have the ability to distinguish different classes. That is to say, it is...
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