Modeling Human Development: Effects of Blurred Vision on Category Learning in CN

Modeling Human Development: Effects of Blurred Vision on Category Learning in CNNs

Recently, training convolutional neural networks (CNNs) using blurry images has been identified as a potential means to produce more robust models for facial recognition (Vogelsang et al. 2018). This method of training is intended to mimic biological visual development, as human visual acuity develops from near-blindness to normal acuity in the first three to four months of life (Kugelberg 1992).Object recognition develops in tandem during this time, and this developmental period has been shown to be critical for many visual tasks in later childhood and adulthood. We explore the effects of training CNNs on images with different levels of applied blur, including training regimens with progressively less blurry training sets. Using subsets of ImageNet (Russakovsky 2015), CNN performance is evaluated for both broad object recognition and fine-grained classification tasks. Results for AlexNet (Krizhevsky et al. 2012) and the more compact SqueezeNet (Iandola et al. 2016) are compared. Using blurry images for training on their own or as part of a training sequence increases classification accuracy across collections of images with different resolutions. At the same time, blurry training data causes little change to training convergence time and false positive classification certainty. Our findings support the utility of learning from sequences of blurry images for more robust image recognition.

模拟人类发展：视觉模糊对CNN类别学习的影响

最近，使用模糊图像训练卷积神经网络（CNN）已被识别为产生更强大的面部识别模型的潜在手段（Vogelsang et al.2018）。这种训练方法旨在模仿生物视觉的发展，因为人类的视力在生命的前三到四个月中从近视力发展到正常视力（Kugelberg 1992）。.. 在此期间，物体识别是一齐发展的，这一发展时期已被证明对于童年和成年后期的许多视觉任务至关重要。我们探讨了训练CNN对具有不同水平的应用模糊的图像的影响，包括使用逐渐减少的模糊训练集的训练方案。使用ImageNet的子集（Russakovsky，2015年），可以针对广泛的对象识别和细粒度分类任务评估CNN性能。比较了AlexNet（Krizhevsky等人，2012）和更紧凑的SqueezeNet（Iandola等人，2016）的结果。使用模糊图像单独或作为训练序列的一部分进行训练，可以提高具有不同分辨率的图像集合的分类精度。与此同时，模糊的训练数据对训练收敛时间和错误肯定的分类确定性影响很小。我们的发现支持从模糊图像序列中进行学习的功能，以实现更强大的图像识别。 （阅读更多）