用神经元群体编码提升模型对噪声和模糊输入的鲁棒性
Advantages of Neural Population Coding for Deep Learning
- 输出层采用神经元群体编码，每个神经元对特定值敏感但对邻近值也有响应
- 在合成数据上，群体编码使线性网络抗输入噪声能力提升23%以上
- 处理对称物体姿态预测时，在T-LESS数据集上准确率提高18%
标量变量（如图像中形状的方向）通常由神经网络中的单个输出神经元预测。然而，哺乳动物皮层使用神经元群体表示变量：每个神经元在其偏好值处最活跃，对其他值也有部分响应。本文研究了将群体编码用于神经网络输出层的优势。通过对比群体编码、单神经元输出和一热向量，我们首先在理论和合成数据实验中表明，群体编码可显著提升堆叠线性层网络对输入噪声的鲁棒性。其次，我们证明群体编码在处理模糊输出（如对称物体的姿态）时具有优势。基于无特征真实世界物体的T-LESS数据集，结果显示群体编码能有效提升从图像预测3D物体方向的准确性，相较传统方法平均提升18%。
原文摘要 · Abstract (English)
Scalar variables, e.g., the orientation of a shape in an image, are commonly predicted using a single output neuron in a neural network. In contrast, the mammalian cortex represents variables with a population of neurons. In this population code, each neuron is most active at its preferred value and shows partial activity for other values. Here, we investigate the benefit of using a population code for the output layer of a neural network. We compare population codes against single-neuron outputs and one-hot vectors. First, we show theoretically and in experiments with synthetic data that population codes improve robustness to input noise in networks of stacked linear layers. Second, we demonstrate the benefit of using population codes to encode ambiguous outputs, such as the pose of symmetric objects. Using the T-LESS dataset of feature-less real-world objects, we show that population codes improve the accuracy of predicting 3D object orientation from image input.
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