A Space-Variant Visual Pathway Model for Data Efficient Deep Learning

Piotr Ozimek, Nina Hristozova, Lorinc Balog, Jan Paul Siebert
<span title="2019-03-26">2019</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/n5oku2uutza7dou2nqqzxhetqy" style="color: black;">Frontiers in Cellular Neuroscience</a> </i> &nbsp;
We present an investigation into adopting a model of the retino-cortical mapping, found in biological visual systems, to improve the efficiency of image analysis using Deep Convolutional Neural Nets (DCNNs) in the context of robot vision and egocentric perception systems. This work has now enabled DCNNs to process input images approaching one million pixels in size, in real time, using only consumer grade graphics processor (GPU) hardware in a single pass of the DCNN.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fncel.2019.00036">doi:10.3389/fncel.2019.00036</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/30971891">pmid:30971891</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6444208/">pmcid:PMC6444208</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mqssy7cdwff33gx3em552brwea">fatcat:mqssy7cdwff33gx3em552brwea</a> </span>
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