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Found 6 papers, 4 papers with code
An adaptive homeostatic algorithm for the unsupervised learning of visual features
The formation of structure in the brain, that is, of the connections between cells within neural populations, is by large an unsupervised learning process: the emergence of this architecture is mostly self-organized.
Beyond $\ell_1$ sparse coding in V1
Compared to other methods, soft thresholding achieves this level of sparsity at the expense of much degraded reconstruction performance, that more likely than not is not acceptable in biological vision.
Effect of top-down connections in Hierarchical Sparse Coding
In this study, a new model called 2-Layers Sparse Predictive Coding (2L-SPC) is introduced to assess the impact of this inter-layer feedback connection.
Sparse Deep Predictive Coding captures contour integration capabilities of the early visual system
Third, we demonstrate at the representational level that the SDPC feedback connections are able to overcome noise in input images.
Sparse models for Computer Vision
Applying such a paradigm to computer vision therefore seems a promising approach towards more biomimetic algorithms.
Differential response of the retinal neural code with respect to the sparseness of natural images
These recordings showed in particular that the reliability of spike timings varies with respect to the sparseness with globally a similar trend than the distribution of sparseness statistics observed in natural images.
