High-contrast "gaudy" images improve the training of deep neural network models of visual cortex
High-contrast "gaudy" images improve the training of deep neural network models of visual cortex
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发表时间：
2020-06
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通讯作者：
Benjamin R. Cowley;Jonathan W. Pillow
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作者：
Benjamin R. Cowley;Jonathan W. Pillow
理解视觉系统的感觉转换的一个关键挑战是获得视觉皮层神经元反应的高度预测模型。深度神经网络（DNN）为这种模型提供了一个有希望的候选者。然而，DNN需要比神经科学家从真实的神经元收集的训练数据多几个数量级，因为实验记录时间受到严重限制。这促使我们找到用尽可能少的训练数据训练高度预测DNN的图像。我们提出了花哨的图像-自然图像的高对比度二值化版本-来有效地训练DNN。在广泛的模拟实验中，我们发现用花哨的图像训练DNN大大减少了准确预测视觉皮层神经元模拟反应所需的训练图像数量。我们还发现，在训练之前选择的花哨图像，优于主动学习算法在训练期间选择的图像。因此，华而不实的图像过分强调自然图像的特征，特别是边缘，这对于有效训练DNN来说是最重要的。我们相信，花哨的图像将有助于视觉皮层神经元的建模，可能会开启关于视觉处理的新科学问题，并帮助寻求改善DNN训练方法的全科医生。
A key challenge in understanding the sensory transformations of the visual system is to obtain a highly predictive model of responses from visual cortical neurons. Deep neural networks (DNNs) provide a promising candidate for such a model. However, DNNs require orders of magnitude more training data than neuroscientists can collect from real neurons because experimental recording time is severely limited. This motivates us to find images that train highly-predictive DNNs with as little training data as possible. We propose gaudy images---high-contrast binarized versions of natural images---to efficiently train DNNs. In extensive simulation experiments, we find that training DNNs with gaudy images substantially reduces the number of training images needed to accurately predict the simulated responses of visual cortical neurons. We also find that gaudy images, chosen before training, outperform images chosen during training by active learning algorithms. Thus, gaudy images overemphasize features of natural images, especially edges, that are the most important for efficiently training DNNs. We believe gaudy images will aid in the modeling of visual cortical neurons, potentially opening new scientific questions about visual processing, as well as aid general practitioners that seek ways to improve the training of DNNs.