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Sparse hierarchical models of vision

In recent years, deep convolutional neural networks (CNNs), which are based 
on the hierarchical structure of the visual cortex has found remarkable accuracy 
rates in object classification. One of its drawbacks is the requirement of a large 
collection of labelled data for training. 
Therefore, unsupervised hierarchical networks are more suited for a more biologically 
plausible model. For a classification accuracy to be closer towards CNNs, 
the invariance and selectivity of the extracted features need to be improved. One 
of the standard methods for learning invariant features is to apply non-linearity 
functions to the data which has been implemented in both CNNs and HMAX 
models. Based on these principles, an extended form of the HMAX model is proposed 
which applies two different types of non-linear pooling operations. 
The extension is designed with the help of sparsity based algorithms such as Independent 
Subspace Analysis (ISA) and Topographic Independent Component 
Analysis(TICA). Aside from an improved classification accuracy compared to 
previous unsupervised hierarchical models, it also reduces the data dimensions 
within the layers.