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Gradients as a Measure of Uncertainty in Neural Networks
We demonstrate the effectiveness of gradients as a measure of model uncertainty in applications of detecting unfamiliar inputs, including out-of-distribution and corrupted samples.
Object Recognition under Multifarious Conditions: A Reliability Analysis and A Feature Similarity-based Performance Estimation
Experimental results show that deep learning-based image representations can estimate the recognition performance variation with a Spearman's rank-order correlation of 0. 94 under multifarious acquisition conditions.
CURE-OR: Challenging Unreal and Real Environments for Object Recognition
Moreover, we investigate the relationship between object recognition and image quality and show that objective quality algorithms can estimate recognition performance under certain photometric challenging conditions.
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