Unsupervised hashing has attracted much attention for binary representation learning due to the requirement of economical storage and efficiency codes. It aims encode high-dimensional features in Hamming space with similarity preservation between instances. However, most existing methods learn hash functions manifold-based approaches. Those capture local geometric structures (i.e., pairwise rel...

In representation learning, Convolutional Sparse Coding (CSC) enables unsupervised learning of features by jointly optimising both an $\ell _{2}$ -norm fidelity term and a sparsity enforcing penalty. This work investigates using regul...