We investigate the performance of a convolutional neural network (CNN) at detecting a signal-known-exactly in Poisson noise. We compare the network performance with that of a Bayesian ideal observer (IO) that has the theoretical optimum in detection performance and a linear support vector machine (SVM). For several types of stimuli, including harmonics, faces, and certain regular patterns, the CNN performance asymptotes at the level of the IO. The SVM detection sensitivity is approximately 3-times lower. For other stimuli, including random patterns and certain cellular automata, the CNN sensitivity is significantly lower than that of the IO and the SVM. Finally, when the signal can appear in one of multiple locations, CNN sensitivity continues to match the ideal sensitivity.
翻译:我们调查一个革命神经网络(CNN)在探测波瓦森噪音中已知的信号时的性能;我们将网络性能与一个在检测性能和线性支持矢量机(SVM)方面理论上最优的巴伊西亚理想观察家(IO)的网络性能进行比较;对于包括口音、脸部和某些常规模式在内的几种刺激,有线电视新闻网在IO一级的性能微弱。SVM的灵敏度大约低3倍。对于其他的灵敏度,包括随机模式和某些蜂窝自动成型,CNN的灵敏度大大低于IO和SVM的灵敏度。最后,当信号出现在多个地点之一时,CN的灵敏度仍然符合理想的灵敏度。