Stochastic Nonlinearities Improve Uncertainty Estimation

The paper introduces stochastic activation switching, a lightweight technique that randomly selects between multiple nonlinear activation functions during inference. This approach generates predictive diversity without disabling intermediate features, enabling uncertainty estimation from a single trained model. Experiments on classification, semantic segmentation, and out‑of‑distribution detection with both convolutional and transformer architectures show improved uncertainty quality while maintaining competitive primary‑task performance.

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• llms
• computer-vision