给低对比度图像加噪声，能提升深度学习模型识别能力
Stochastic Resonance Improves the Detection of Low Contrast Images in Deep Learning Models
- 在LSTM网络中引入可控噪声，增强弱信号检测
- 降低图像对比度后，加入噪声使分类准确率部分恢复
- 首次验证脉冲速率神经网络存在随机共振现象
随机共振描述了在某些系统中噪声有助于增强微弱信号的可检测性。尽管在自然和工程系统中广泛存在，但其在基于脉冲速率的神经网络图像分类中的作用尚未深入研究。本研究使用一个简单的LSTM循环神经网络进行数字识别与分类任务。测试阶段将图像对比度降低至模型无法识别刺激的程度，随后引入可控噪声以部分恢复分类性能。结果表明，在基于脉冲速率的循环神经网络中确实存在随机共振现象。
原文摘要 · Abstract (English)
Stochastic resonance describes the utility of noise in improving the detectability of weak signals in certain types of systems. It has been observed widely in natural and engineered settings, but its utility in image classification with rate-based neural networks has not been studied extensively. In this analysis a simple LSTM recurrent neural network is trained for digit recognition and classification. During the test phase, image contrast is reduced to a point where the model fails to recognize the presence of a stimulus. Controlled noise is added to partially recover classification performance. The results indicate the presence of stochastic resonance in rate-based recurrent neural networks.
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