Sparse training has emerged as a promising method for resource-efficient deep
neural networks (DNNs) in real-world applications. However, the reliability of
sparse models remains a crucial concern, particularly in detecting unknown
out-of-distribution (OOD) data. This study addresses the knowledge gap by
investigating the reliability of sparse training from an OOD perspective and
reveals that sparse training exacerbates OOD unreliability. The lack of unknown
information and the sparse constraints hinder the effective exploration of
weight space and accurate differentiation between known and unknown knowledge.
To tackle these challenges, we propose a new unknown-aware sparse training
method, which incorporates a loss modification, auto-tuning strategy, and a
voting scheme to guide weight space exploration and mitigate confusion between
known and unknown information without incurring significant additional costs or
requiring access to additional OOD data. Theoretical insights demonstrate how
our method reduces model confidence when faced with OOD samples. Empirical
experiments across multiple datasets, model architectures, and sparsity levels
validate the effectiveness of our method, with improvements of up to
\textbf{8.4\%} in AUROC while maintaining comparable or higher accuracy and
calibration. This research enhances the understanding and readiness of sparse
DNNs for deployment in resource-limited applications. Our code is available on:
https://github.com/StevenBoys/MOON.

稀疏训练是一种在实际应用中资源高效的深度神经网络方法，然而，本研究揭示了稀疏训练加剧了对未知样本的不可靠性，通过提出一种新的对未知样本敏感的稀疏训练方法，我们改进了模型的准确性和可靠性，有效降低了面对未知样本时的模型自信度。