Does training with blurred images bring convolutional neural networks closer to humans with respect to robust object recognition and internal representations?
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 wit...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Psychology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1047694/full |
| _version_ | 1797924489354280960 |
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| author | Sou Yoshihara Taiki Fukiage Shin'ya Nishida Shin'ya Nishida |
| author_facet | Sou Yoshihara Taiki Fukiage Shin'ya Nishida Shin'ya Nishida |
| author_sort | Sou Yoshihara |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T15:01:59Z |
| format | Article |
| id | doaj.art-5f616dce618a4daca03daf2e42e4f22c |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-04-10T15:01:59Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-5f616dce618a4daca03daf2e42e4f22c2023-02-15T09:53:20ZengFrontiers Media S.A.Frontiers in Psychology1664-10782023-02-011410.3389/fpsyg.2023.10476941047694Does training with blurred images bring convolutional neural networks closer to humans with respect to robust object recognition and internal representations?Sou Yoshihara0Taiki Fukiage1Shin'ya Nishida2Shin'ya Nishida3Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Kyoto, JapanNTT Communication Science Laboratories, Nippon Telegraph and Telephone Corporation, Atsugi, JapanDepartment of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Kyoto, JapanNTT Communication Science Laboratories, Nippon Telegraph and Telephone Corporation, Atsugi, JapanIt 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.https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1047694/fullconvolutional neural networksobject recognitionvisual developmentperceptual organizationoptical blur |
| spellingShingle | Sou Yoshihara Taiki Fukiage Shin'ya Nishida Shin'ya Nishida Does training with blurred images bring convolutional neural networks closer to humans with respect to robust object recognition and internal representations? Frontiers in Psychology convolutional neural networks object recognition visual development perceptual organization optical blur |
| title | Does training with blurred images bring convolutional neural networks closer to humans with respect to robust object recognition and internal representations? |
| title_full | Does training with blurred images bring convolutional neural networks closer to humans with respect to robust object recognition and internal representations? |
| title_fullStr | Does training with blurred images bring convolutional neural networks closer to humans with respect to robust object recognition and internal representations? |
| title_full_unstemmed | Does training with blurred images bring convolutional neural networks closer to humans with respect to robust object recognition and internal representations? |
| title_short | Does training with blurred images bring convolutional neural networks closer to humans with respect to robust object recognition and internal representations? |
| title_sort | does training with blurred images bring convolutional neural networks closer to humans with respect to robust object recognition and internal representations |
| topic | convolutional neural networks object recognition visual development perceptual organization optical blur |
| url | https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1047694/full |
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