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Found 5 papers, 2 papers with code
Epistemic Graph: A Plug-And-Play Module For Hybrid Representation Learning
In this paper, a novel Epistemic Graph Layer (EGLayer) is introduced to enable hybrid learning, enhancing the exchange of information between deep features and a structured knowledge graph.
TaxoKnow: Taxonomy as Prior Knowledge in the Loss Function of Multi-class Classification
Our obtained results show the significant effect of a taxonomy in increasing the performance of a learner in semisupervised multi-class classification and the considerable results obtained in a fully supervised fashion.
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.
Self-Supervised Graph Neural Network for Multi-Source Domain Adaptation
Domain adaptation (DA) tries to tackle the scenarios when the test data does not fully follow the same distribution of the training data, and multi-source domain adaptation (MSDA) is very attractive for real world applications.
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).
