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Found 6 papers, 3 papers with code
Structure Your Data: Towards Semantic Graph Counterfactuals
With a focus on the visual domain, we represent images as scene graphs and obtain their GNN embeddings to bypass solving the NP-hard graph similarity problem for all input pairs, an integral part of the CE computation process.
Choose your Data Wisely: A Framework for Semantic Counterfactuals
In this work, we build on these ideas, and propose a framework that provides counterfactual explanations in terms of knowledge graphs.
Fine-Grained ImageNet Classification in the Wild
Image classification has been one of the most popular tasks in Deep Learning, seeing an abundance of impressive implementations each year.
Counterfactual Edits for Generative Evaluation
Evaluation of generative models has been an underrepresented field despite the surge of generative architectures.
Conceptual Edits as Counterfactual Explanations
We propose a framework for generating counterfactual explanations of black-box classifiers, which answer the question “What has to change for this to be classified as X instead of Y?” in terms of given domain knowledge.
Semantic Enrichment of Pretrained Embedding Output for Unsupervised IR
The rapid growth of scientific literature in the biomedical and clinical domain has significantly com- plicated the identification of information of interest by researchers as well as other practitioners.
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