Studying the Impact of Latent Representations in Implicit Neural Networks for Scientific Continuous Field Reconstruction
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913307612413952 |
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| author | Xu, Wei DeSantis, Derek Freeman Luo, Xihaier Parmar, Avish Tan, Klaus Nadiga, Balu Ren, Yihui Yoo, Shinjae |
| author_facet | Xu, Wei DeSantis, Derek Freeman Luo, Xihaier Parmar, Avish Tan, Klaus Nadiga, Balu Ren, Yihui Yoo, Shinjae |
| contents | Learning a continuous and reliable representation of physical fields from sparse sampling is challenging and it affects diverse scientific disciplines. In a recent work, we present a novel model called MMGN (Multiplicative and Modulated Gabor Network) with implicit neural networks. In this work, we design additional studies leveraging explainability methods to complement the previous experiments and further enhance the understanding of latent representations generated by the model. The adopted methods are general enough to be leveraged for any latent space inspection. Preliminary results demonstrate the contextual information incorporated in the latent representations and their impact on the model performance. As a work in progress, we will continue to verify our findings and develop novel explainability approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_06418 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Studying the Impact of Latent Representations in Implicit Neural Networks for Scientific Continuous Field Reconstruction Xu, Wei DeSantis, Derek Freeman Luo, Xihaier Parmar, Avish Tan, Klaus Nadiga, Balu Ren, Yihui Yoo, Shinjae Machine Learning Artificial Intelligence Learning a continuous and reliable representation of physical fields from sparse sampling is challenging and it affects diverse scientific disciplines. In a recent work, we present a novel model called MMGN (Multiplicative and Modulated Gabor Network) with implicit neural networks. In this work, we design additional studies leveraging explainability methods to complement the previous experiments and further enhance the understanding of latent representations generated by the model. The adopted methods are general enough to be leveraged for any latent space inspection. Preliminary results demonstrate the contextual information incorporated in the latent representations and their impact on the model performance. As a work in progress, we will continue to verify our findings and develop novel explainability approaches. |
| title | Studying the Impact of Latent Representations in Implicit Neural Networks for Scientific Continuous Field Reconstruction |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2404.06418 |