Comparison of Embedded Spaces for Deep Learning Classification
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917740715966464 |
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| author | Scholl, Stefan |
| author_facet | Scholl, Stefan |
| contents | Embedded spaces are a key feature in deep learning. Good embedded spaces represent the data well to support classification and advanced techniques such as open-set recognition, few-short learning and explainability. This paper presents a compact overview of different techniques to design embedded spaces for classification. It compares different loss functions and constraints on the network parameters with respect to the achievable geometric structure of the embedded space. The techniques are demonstrated with two and three-dimensional embeddings for the MNIST, Fashion MNIST and CIFAR-10 datasets, allowing visual inspection of the embedded spaces. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_01767 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Comparison of Embedded Spaces for Deep Learning Classification Scholl, Stefan Machine Learning Computer Vision and Pattern Recognition Image and Video Processing Signal Processing Embedded spaces are a key feature in deep learning. Good embedded spaces represent the data well to support classification and advanced techniques such as open-set recognition, few-short learning and explainability. This paper presents a compact overview of different techniques to design embedded spaces for classification. It compares different loss functions and constraints on the network parameters with respect to the achievable geometric structure of the embedded space. The techniques are demonstrated with two and three-dimensional embeddings for the MNIST, Fashion MNIST and CIFAR-10 datasets, allowing visual inspection of the embedded spaces. |
| title | Comparison of Embedded Spaces for Deep Learning Classification |
| topic | Machine Learning Computer Vision and Pattern Recognition Image and Video Processing Signal Processing |
| url | https://arxiv.org/abs/2408.01767 |