Tag: multitask learning

by Synced 2023-05-10 1
AI Computer Vision & Graphics Machine Learning & Data Science Research

Georgia Tech’s ZipIt! Effectively Merges Vision Models Trained on Disjoint Tasks Without Additional Training

In the new paper ZipIt! Merging Models from Different Tasks Without Training, a Georgia Tech research team proposes ZipIt!, a general method that exploits redundant features to combine two or more models with the same architecture but trained on different tasks into one multi-task model without additional training.

by Synced 2022-04-08 Number of comments0
AI Machine Learning & Data Science Research

EPFL’s Multi-modal Multi-task Masked Autoencoder: A Simple, Flexible and Effective ViT Pretraining Strategy Applicable to Any RGB Dataset

The Swiss Federal Institute of Technology Lausanne (EPFL) presents Multi-modal Multi-task Masked Autoencoders (MultiMAE), a simple and effective pretraining strategy that enables masked autoencoding to include multiple modalities and tasks and is applicable to any RGB dataset.