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Found 9 papers, 0 papers with code
Generalizable Semantic Vision Query Generation for Zero-shot Panoptic and Semantic Segmentation
However, one of the gaps in synthesizing pseudo vision queries, ie, vision queries for unseen categories, is describing fine-grained visual details through semantic embeddings.
CLIP Is Also a Good Teacher: A New Learning Framework for Inductive Zero-shot Semantic Segmentation
Moreover, to enhance the ability to discriminate unseen categories, PLM consisting of pseudo labels and weight generation is designed.
Deep Generative Imputation Model for Missing Not At Random Data
Existing statistical methods model the MNAR mechanism by different decomposition of the joint distribution of the complete data and the missing mask.
Project Florida: Federated Learning Made Easy
Federated learning enables model training across devices and silos while the training data remains within its security boundary, by distributing a model snapshot to a client running inside the boundary, running client code to update the model, and then aggregating updated snapshots across many clients in a central orchestrator.
Federated Multilingual Models for Medical Transcript Analysis
Federated Learning (FL) is a novel machine learning approach that allows the model trainer to access more data samples, by training the model across multiple decentralized data sources, while data access constraints are in place.
Uncertainty-Aware Deep Co-training for Semi-supervised Medical Image Segmentation
However, due to the scarcity of labeled data, the features extracted by the models are limited in supervised learning, and the quality of predictions for unlabeled data also cannot be guaranteed.
APIK: Active Physics-Informed Kriging Model with Partial Differential Equations
We present in this work a PDE Informed Kriging model (PIK), which introduces PDE information via a set of PDE points and conducts posterior prediction similar to the standard kriging method.
Active Image Synthesis for Efficient Labeling
The great success achieved by deep neural networks attracts increasing attention from the manufacturing and healthcare communities.
Generative Invertible Networks (GIN): Pathophysiology-Interpretable Feature Mapping and Virtual Patient Generation
Machine learning methods play increasingly important roles in pre-procedural planning for complex surgeries and interventions.
