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Found 6 papers, 1 papers with code
Graph vs. Sequence: An Empirical Study on Knowledge Forms for Knowledge-Grounded Dialogue
Knowledge-grounded dialogue is a task of generating an informative response based on both the dialogue history and external knowledge source.
FeaInfNet: Diagnosis in Medical Image with Feature-Driven Inference and Visual Explanations
Due to the unique multi-instance learning of medical images and the difficulty in identifying decision-making regions, many interpretability models that have been proposed still have problems of insufficient accuracy and interpretability in medical image disease diagnosis.
Hierarchical Dynamic Masks for Visual Explanation of Neural Networks
Saliency methods generating visual explanatory maps representing the importance of image pixels for model classification is a popular technique for explaining neural network decisions.
Decoupling Deep Learning for Interpretable Image Recognition
It decouples the inference and interpretation modules of a prototype-based network by avoiding the use of prototype activation to explain the network's decisions in order to simultaneously improve the accuracy and interpretability of the neural network.
MDM: Multiple Dynamic Masks for Visual Explanation of Neural Networks
We applied the MDM method to the interpretable neural networks ProtoPNet and XProtoNet, which improved the performance of model in the explainable prototype search.
Variational Transformer: A Framework Beyond the Trade-off between Accuracy and Diversity for Image Captioning
In this work, we will show that the inferior standard of accuracy draws from human annotations (leave-one-out) are not appropriate for machine-generated captions.
