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Found 3 papers, 2 papers with code
CheXpert++: Approximating the CheXpert labeler for Speed,Differentiability, and Probabilistic Output
CheXpert is very useful, but is relatively computationally slow, especially when integrated with end-to-end neural pipelines, is non-differentiable so can't be used in any applications that require gradients to flow through the labeler, and does not yield probabilistic outputs, which limits our ability to improve the quality of the silver labeler through techniques such as active learning.
3D-Aware Scene Manipulation via Inverse Graphics
In this work, we propose 3D scene de-rendering networks (3D-SDN) to address the above issues by integrating disentangled representations for semantics, geometry, and appearance into a deep generative model.
Unsupervised Domain Adaptation With Imbalanced Cross-Domain Data
For standard unsupervised domain adaptation, one typically obtains labeled data in the source domain and only observes unlabeled data in the target domain.
