  This paper aims to improve the feature learning in Convolutional Networks
(Convnet) by capturing the structure of objects. A new sparsity function is
imposed on the extracted featuremap to capture the structure and shape of the
learned object, extracting interpretable features to improve the prediction
performance. The proposed algorithm is based on organizing the activation
within and across featuremap by constraining the node activities through
$\ell_{2}$ and $\ell_{1}$ normalization in a structured form.
