Do training with blurred images make convolutional neural networks closer to humans concerning object recognition performance and internal representations?

Abstract

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.

Remote intelligent perception system for multi-object detection

“…The authors mentioned that they avoided incorporating higher combinations of classes because they would unnecessarily increase computational complexity without providing significant additional information. Specifically, when determining the predicted class of a patch, we excluded classes that were likely to be confused with the chosen class, reducing complexity while maintaining accuracy ( Yoshihara et al, 2022 ). The study investigates the effect of training convolutional neural networks (CNNs) on ImageNet object recognition using a combination of sharp and blurry images.…”