FIGURE 7 Filter learned in the first layer on the CIFAR-10 dataset by
traditional CAE (no pooling and 0% noise) and denoising CAE (no pooling
and 50% Gaussian noise)
4.1.3 | Convolutional Sparse coding
Sparse coding is a group of biologically plausible algorithms that leans
the basis function to capture the high-level features from the
unlabelled dataset. In Sparse coding, a sparse vector is computed
through the linear operation with a learned dictionary matrix for the
optimum input reconstruction. Sparse coding was compared with batch
alternatives and claimed to be an efficient feature learner over a large
dataset. However, the traditional sparse coding is computationally
expensive as it is performed over the whole image, and the
representations are often redundant as inferences are performed at the
patch level.