It has been suggested that perceiving blurry images in addition to sharp images contributes to the development of robust human visual processing. To computationally investigate the effect of exposure to blurry images, we trained convolutional neural networks (CNNs) on ImageNet object recognition with a variety of combinations of sharp and blurred images. In agreement with recent reports, mixed training on blurred and sharp images (B+S training) brings CNNs closer to humans with respect to robust object recognition against a change in image blur. B+S training also slightly reduces the texture bias of CNNs in recognition of shape-texture cue conflict images, but the effect is not strong enough to achieve human-level shape bias. Other tests also suggest that B+S training cannot produce robust human-like object recognition based on global configuration features. Using representational similarity analysis and zero-shot transfer learning, we also show that B+S-Net does not facilitate blur-robust object recognition through separate specialized sub-networks, one network for sharp images and another for blurry images, but through a single network analyzing image features common across sharp and blurry images. However, blur training alone does not automatically create a mechanism like the human brain in which sub-band information is integrated into a common representation. Our analysis suggests that experience with blurred images may help the human brain recognize objects in blurred images, but that alone does not lead to robust, human-like object recognition.

It is suggested that experiences of perceiving blurry images in addition to sharp images contribute to the development of robust human visual processing. To computationally investigate the effect of exposure to blurry images, we trained Convolutional Neural Networks (CNNs) on ImageNet object recognition with a variety of combinations of sharp and blurry images. In agreement with related studies, mixed training on sharp and blurred images (B+S) makes the CNNs close to humans with respect to robust object recognition against a change in image blur. B+S training also reduces the texture bias of CNN in recognition of shape-texture-cue-conflict images, but the effect is not strong enough to achieve a strong shape bias comparable to what humans show. Other tests also suggest that B+S training is not sufficient to produce robust human-like object recognition based on global configurational features. We also show using representational similarity analysis and zero-shot transfer learning that B+S-Net does not acquire blur-robust object recognition through separate specialized sub-networks, each for sharp and blurry images, but through a single network analyzing common image features. However, blur training alone does not automatically create a mechanism like the human brain where subband information is integrated into a common representation. Our analyses suggest that experience with blurred images helps the human brain develop neural networks that robustly recognize the surrounding world, but it is not powerful enough to fill a large gap between humans and CNNs.

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