Deep Sparse Representation-based Classification

24 Apr 2019  ·  Mahdi Abavisani, Vishal M. Patel ·

We present a transductive deep learning-based formulation for the sparse representation-based classification (SRC) method. The proposed network consists of a convolutional autoencoder along with a fully-connected layer. The role of the autoencoder network is to learn robust deep features for classification. On the other hand, the fully-connected layer, which is placed in between the encoder and the decoder networks, is responsible for finding the sparse representation. The estimated sparse codes are then used for classification. Various experiments on three different datasets show that the proposed network leads to sparse representations that give better classification results than state-of-the-art SRC methods. The source code is available at:

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Sparse Representation-based Classification SVHN DSRC Accuracy 67.75 # 1