Brain-like emergent properties in deep networks: impact of network architecture, datasets and training
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866908704554614784 |
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| author | Rajesh, Niranjan Jacob, Georgin Arun, SP |
| author_facet | Rajesh, Niranjan Jacob, Georgin Arun, SP |
| contents | Despite the rapid pace at which deep networks are improving on standardized vision benchmarks, they are still outperformed by humans on real-world vision tasks. One solution to this problem is to make deep networks more brain-like. Although there are several benchmarks that compare the ability of deep networks to predict brain responses on natural images, they do not capture subtle but important emergent properties present in brains. It is also unclear which design principle -- architecture, training data, or training regime -- would have the greatest impact on these emergent properties. To investigate these issues, we systematically evaluated over 30 state-of-the-art networks with varying network architectures, training datasets, and training regimes for the presence or absence of brain-like properties. Our main findings are as follows. First, network architecture had the strongest impact on brain-like properties compared to dataset and training regime variations. Second, networks varied widely in their alignment to the brain with no single network outperforming all others. Taken together, our results offer a principled and interpretable path toward closing the gap between artificial and human vision. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_16326 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Brain-like emergent properties in deep networks: impact of network architecture, datasets and training Rajesh, Niranjan Jacob, Georgin Arun, SP Computer Vision and Pattern Recognition Artificial Intelligence Despite the rapid pace at which deep networks are improving on standardized vision benchmarks, they are still outperformed by humans on real-world vision tasks. One solution to this problem is to make deep networks more brain-like. Although there are several benchmarks that compare the ability of deep networks to predict brain responses on natural images, they do not capture subtle but important emergent properties present in brains. It is also unclear which design principle -- architecture, training data, or training regime -- would have the greatest impact on these emergent properties. To investigate these issues, we systematically evaluated over 30 state-of-the-art networks with varying network architectures, training datasets, and training regimes for the presence or absence of brain-like properties. Our main findings are as follows. First, network architecture had the strongest impact on brain-like properties compared to dataset and training regime variations. Second, networks varied widely in their alignment to the brain with no single network outperforming all others. Taken together, our results offer a principled and interpretable path toward closing the gap between artificial and human vision. |
| title | Brain-like emergent properties in deep networks: impact of network architecture, datasets and training |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2411.16326 |