arXiv:2604.09734v3 Announce Type: replace-cross
Abstract: We introduce an unsupervised visual representation learning system based entirely on local plasticity rules, without labels, backpropagation, or global error signals. The model is a VisNet-insp…
arXiv:2604.09734v2 Announce Type: replace
Abstract: We study how far structured architectural bias can compensate for the absence of end-to-end gradient-based representation learning in visual recognition. Building on the VisNet tradition, we introduc…
arXiv:2604.09734v1 Announce Type: cross
Abstract: We study how far structured architectural bias can compensate for the absence of end-to-end gradient-based representation learning in visual recognition. Building on the VisNet tradition, we introduce …