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Feb, 2023
V1的稀疏几何自编码器模型
Sparse, Geometric Autoencoder Models of V1
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Jonathan Huml, Abiy Tasissa, Demba Ba
TL;DR
该论文提出了一种基于自编码器的结构稀疏方法,可以更好地匹配灵长类数据,使用加权L1约束的自编码器目标函数保留了稀疏编码框架的核心思想。
Abstract
The classical
sparse coding
model represents visual stimuli as a linear combination of a handful of learned basis functions that are Gabor-like when trained on natural image data. However, the Gabor-like filters learned by classical
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