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Found 3 papers, 2 papers with code
PICNN: A Pathway towards Interpretable Convolutional Neural Networks
Experimental results have demonstrated that our method PICNN (the combination of standard CNNs with our proposed pathway) exhibits greater interpretability than standard CNNs while achieving higher or comparable discrimination power.
Incorporating Heterophily into Graph Neural Networks for Graph Classification
Graph neural networks (GNNs) often assume strong homophily in graphs, seldom considering heterophily which means connected nodes tend to have different class labels and dissimilar features.
Training and Inference for Integer-Based Semantic Segmentation Network
However, neural network quantization can be used to reduce computation load while maintaining comparable accuracy and original network structure.
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