Density-Softmax: Efficient Test-time Model for Uncertainty Estimation and Robustness under Distribution Shifts

• URL: http://arxiv.org/abs/2302.06495v3
• Date: Tue, 28 May 2024 02:27:54 GMT
• Title: Density-Softmax: Efficient Test-time Model for Uncertainty Estimation and Robustness under Distribution Shifts
• Authors: Ha Manh Bui, Anqi Liu,
• Abstract summary: Density-Softmax is a sampling-free deterministic framework for uncertainty estimation. We show that our model is the solution of minimax uncertainty risk. Our method enjoys competitive results with state-of-the-art techniques in terms of uncertainty and robustness.
• Score: 8.431465371266391
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sampling-based methods, e.g., Deep Ensembles and Bayesian Neural Nets have become promising approaches to improve the quality of uncertainty estimation and robust generalization. However, they suffer from a large model size and high latency at test-time, which limits the scalability needed for low-resource devices and real-time applications. To resolve these computational issues, we propose Density-Softmax, a sampling-free deterministic framework via combining a density function built on a Lipschitz-constrained feature extractor with the softmax layer. Theoretically, we show that our model is the solution of minimax uncertainty risk and is distance-aware on feature space, thus reducing the over-confidence of the standard softmax under distribution shifts. Empirically, our method enjoys competitive results with state-of-the-art techniques in terms of uncertainty and robustness, while having a lower number of model parameters and a lower latency at test-time.

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