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Found 6 papers, 2 papers with code
Probing the Purview of Neural Networks via Gradient Analysis
We define purview as the additional capacity necessary to characterize inference samples that differ from the training data.
Gradient-Based Adversarial and Out-of-Distribution Detection
We propose to utilize gradients for detecting adversarial and out-of-distribution samples.
Open-Set Recognition with Gradient-Based Representations
Neural networks for image classification tasks assume that any given image during inference belongs to one of the training classes.
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.
