Deep Sparse Coding for Invariant Multimodal Halle Berry Neurons
Deep Sparse Coding for Invariant Multimodal Halle Berry Neurons
复制标题
不变多模态 Halle Berry 神经元的深度稀疏编码
DOI：
10.1109/cvpr.2018.00122
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Garrett T. Kenyon
中科院分区：
文献类型：
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作者：
Edward Kim;Darryl Hannan;Garrett T. Kenyon
深度前馈卷积神经网络(CNN)已经在几乎所有的机器学习和计算机视觉挑战中变得无处不在；然而，CNN的进步可以说已经达到了工程饱和点，在这个点上，增量的新颖性会带来很小的性能改进。尽管有证据表明，在狭义定义的任务中，物体分类已经达到了人类的水平，但对于一般应用来说，生物视觉系统远远优于任何计算机。研究表明，在哺乳动物的视觉中，前馈深度神经网络中有许多缺失的组件。大脑不仅以前馈方式工作，而且所有神经元都在相互竞争；神经元以自下而上和自上而下的方式整合信息，并在建模过程中纳入预期和反馈。此外，我们的视觉皮质正在与我们的顶叶协同工作，整合来自各种形式的感觉信息。
Deep feed-forward convolutional neural networks (CNNs) have become ubiquitous in virtually all machine learning and computer vision challenges; however, advancements in CNNs have arguably reached an engineering saturation point where incremental novelty results in minor performance gains. Although there is evidence that object classification has reached human levels on narrowly defined tasks, for general applications, the biological visual system is far superior to that of any computer. Research reveals there are numerous missing components in feed-forward deep neural networks that are critical in mammalian vision. The brain does not work solely in a feed-forward fashion, but rather all of the neurons are in competition with each other; neurons are integrating information in a bottom up and top down fashion and incorporating expectation and feedback in the modeling process. Furthermore, our visual cortex is working in tandem with our parietal lobe, integrating sensory information from various modalities.