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Found 6 papers, 2 papers with code
A Bi-Pyramid Multimodal Fusion Method for the Diagnosis of Bipolar Disorders
Previous research on the diagnosis of Bipolar disorder has mainly focused on resting-state functional magnetic resonance imaging.
AGAIN: Adversarial Training With Attribution Span Enlargement and Hybrid Feature Fusion
To this end, compared with standard DNNs, we discover that the generalization gap of adversarially trained DNNs is caused by the smaller attribution span on the input image.
Window Normalization: Enhancing Point Cloud Understanding by Unifying Inconsistent Point Densities
Existing methods are limited by the inconsistent point densities of different parts in the point cloud.
Ranked #6 on
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An Extension of LIME with Improvement of Interpretability and Fidelity
As an extension of LIME, this paper proposes an high-interpretability and high-fidelity local explanation method, known as Local Explanation using feature Dependency Sampling and Nonlinear Approximation (LEDSNA).
A Modified Perturbed Sampling Method for Local Interpretable Model-agnostic Explanation
Explainability is a gateway between Artificial Intelligence and society as the current popular deep learning models are generally weak in explaining the reasoning process and prediction results.
Explaining the Predictions of Any Image Classifier via Decision Trees
Despite outstanding contribution to the significant progress of Artificial Intelligence (AI), deep learning models remain mostly black boxes, which are extremely weak in explainability of the reasoning process and prediction results.
