Improving Fine-Tuning with Latent Cluster Correction

Cédric Ho Thanh

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arXiv · 2501.11919

Improving Fine-Tuning with Latent Cluster Correction

Cédric Ho Thanh

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Abstract

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.

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Cédric Ho Thanh. 2025-01-21. Improving Fine-Tuning with Latent Cluster Correction. https://arxiv.org/abs/2501.11919

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