Mohammad Sarhangzadeh, Amirreza M. Shebly, Doruk Oner · 2026 · arXiv
DOI: 10.48550/arXiv.2610.29875 · open-access PDF
可靠的不确定性估计在视觉识别任务中至关重要,提供模型预测的可靠性和可靠性信息。许多不确定性估计方法通过能够抑制中间特征、扰乱输入或需要大量额外计算的机制获得预测多样性。
Machine translation (Meta M2M100); the English original follows.
Reliable uncertainty estimation is important across visual recognition tasks, providing information about the confidence and reliability of model predictions. Many uncertainty estimation methods obtain predictive diversity through mechanisms that can suppress intermediate features, perturb the input, or require substantial additional computation. Stochastic activation functions, where network units randomly select between multiple nonlinearities during a forward pass, have previously been used to improve optimization behavior, mitigate limitations of fixed ReLU activations, and increase output
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