论文标题
图像识别的新型ANN结构
A Novel ANN Structure for Image Recognition
论文作者
论文摘要
该论文介绍了用于图像识别的新神经模型多层自动共振网络（ARN）。 ARN中的神经元称为节点，锁定在传入模式上，并在输入在其“覆盖范围内”时产生共鸣。共振允许神经元具有噪声和可调性。节点的覆盖范围使他们能够近似传入模式。它的闩锁特性使其可以对情节事件做出响应，而不会干扰现有的训练有素的网络。这些网络能够解决各个领域的问题，但尚未得到充分探索。本文讨论了使用两层ARN实施图像分类和识别系统。 MNIST数据集的识别精度已达到94％，每个数字只有两层神经元，仅50个样本，使其在云基础架构边缘的计算中有用。
The paper presents Multi-layer Auto Resonance Networks (ARN), a new neural model, for image recognition. Neurons in ARN, called Nodes, latch on to an incoming pattern and resonate when the input is within its 'coverage.' Resonance allows the neuron to be noise tolerant and tunable. Coverage of nodes gives them an ability to approximate the incoming pattern. Its latching characteristics allow it to respond to episodic events without disturbing the existing trained network. These networks are capable of addressing problems in varied fields but have not been sufficiently explored. Implementation of an image classification and identification system using two-layer ARN is discussed in this paper. Recognition accuracy of 94% has been achieved for MNIST dataset with only two layers of neurons and just 50 samples per numeral, making it useful in computing at the edge of cloud infrastructure.
