We then propose to employ layout, a very abstract and easy-to-obtain prior, as the spatial guidance for subject arrangement.

Diffusion generative models, as an emerging research focus, have been applied to graph generation tasks.

Machine unlearning has raised significant interest with the adoption of laws ensuring the "right to be forgotten".

More importantly, we employ a generative model for synthetic samples to simulate the intervention of various non-causal effects, thereby minimizing their impact on hash codes for domain invariance. Comprehensive experiments conducted on benchmark datasets confirm the superior performance of our proposed IDEA compared to a variety of competitive baselines.

Hongzhan Lin and Ang Lv proposed the idea of MoICE.

This paper presents an innovative approach to classification tasks called Credal Deep Ensembles (CreDEs), ensembles of novel Credal-Set Neural Networks (CreNets), aiming to improve EU quantification in the framework of credal inference.