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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.11919 |
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| _version_ | 1866909462847029248 |
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| author | Thanh, Cédric Ho |
| author_facet | Thanh, Cédric Ho |
| contents | The existence of salient semantic clusters in the latent spaces of a neural network during training strongly correlates its final accuracy on classification tasks. This paper proposes a novel fine-tuning method that boosts performance by optimising the formation of these latent clusters, using the Louvain community detection algorithm and a specifically designed clustering loss function. We present preliminary results that demonstrate the viability of this process on classical neural network architectures during fine-tuning on the CIFAR-100 dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_11919 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improving Fine-Tuning with Latent Cluster Correction Thanh, Cédric Ho Machine Learning The existence of salient semantic clusters in the latent spaces of a neural network during training strongly correlates its final accuracy on classification tasks. This paper proposes a novel fine-tuning method that boosts performance by optimising the formation of these latent clusters, using the Louvain community detection algorithm and a specifically designed clustering loss function. We present preliminary results that demonstrate the viability of this process on classical neural network architectures during fine-tuning on the CIFAR-100 dataset. |
| title | Improving Fine-Tuning with Latent Cluster Correction |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2501.11919 |