Structured Sparse Convolutional Autoencoder
This work addresses feature interpretability in ConvNets for computer vision tasks, but it appears incremental as it builds on existing sparsity methods.
The paper tackles the problem of improving feature learning in Convolutional Networks by capturing object structure, resulting in enhanced prediction performance through interpretable features.
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.