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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2407.12599 |
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| _version_ | 1866916328430895104 |
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| author | Oubaha, Brahim Berrou, Claude Ji, Xueyao Nasser, Yehya Bidan, Raphaël Le |
| author_facet | Oubaha, Brahim Berrou, Claude Ji, Xueyao Nasser, Yehya Bidan, Raphaël Le |
| contents | Diversity is a concept of prime importance in almost all disciplines based on information processing. In telecommunications, for example, spatial, temporal, and frequency diversity, as well as redundant coding, are fundamental concepts that have enabled the design of extremely efficient systems. In machine learning, in particular with neural networks, diversity is not always a concept that is emphasized or at least clearly identified. This paper proposes a neural network architecture that builds upon various diversity principles, some of them already known, others more original. Our architecture obtains remarkable results, with a record self-supervised learning accuracy of 99. 57% in MNIST, and a top tier promising semi-supervised learning accuracy of 94.21% in CIFAR-10 using only 25 labels per class. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_12599 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On Diversity in Discriminative Neural Networks Oubaha, Brahim Berrou, Claude Ji, Xueyao Nasser, Yehya Bidan, Raphaël Le Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Diversity is a concept of prime importance in almost all disciplines based on information processing. In telecommunications, for example, spatial, temporal, and frequency diversity, as well as redundant coding, are fundamental concepts that have enabled the design of extremely efficient systems. In machine learning, in particular with neural networks, diversity is not always a concept that is emphasized or at least clearly identified. This paper proposes a neural network architecture that builds upon various diversity principles, some of them already known, others more original. Our architecture obtains remarkable results, with a record self-supervised learning accuracy of 99. 57% in MNIST, and a top tier promising semi-supervised learning accuracy of 94.21% in CIFAR-10 using only 25 labels per class. |
| title | On Diversity in Discriminative Neural Networks |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.12599 |