When training data are limited, data-driven models are especially vulnerable to optimization-related fluctuations from random initialization and to sampling-induced bias from insufficient training data. We address both challenges with transfer learning (TL): deep neural networks (DNNs) are first pretrained on $\alpha$-decay half-lives and then fine-tuned on a small cluster-decay dataset. The pretraining stage provides a physically informed initialization that stabilizes optimization, while transferred global-decay systematics regularize the fit and reduce sensitivity to training-set composition. Despite extreme data sparsity, the resulting models accurately predict cluster-decay half-lives for parent nuclei from ${}^{221}\mathrm{Fr}$ to ${}^{242}\mathrm{Cm}$. We further quantify how initialization and sample selection affect predictive accuracy and robustness, demonstrating that TL enables stable and reliable learning in the small-sample regime.

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• Alpha decay
• Nuclear decay
• Nuclear structure & decays
• A ≥ 220
• Machine learning categories