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Taskonomy: Disentangling Task Transfer Learning
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code added code and results Jun 11, 2019
data definition of class 0 (uncertain) in semantic segmentation labels added. Nov 14, 2019
results markup update Jul 23, 2019
taskbank updated download server from aws to stanford's Jun 12, 2019
LICENSE Create LICENSE Dec 18, 2017
README.md affinity shot/link added. Jul 22, 2019

README.md

Taskonomy: Disentangling Task Transfer Learning

This repository contains:

for the the following paper:

Taskonomy: Disentangling Task Transfer Learning (CVPR 2018, Best Paper Award)

Amir Zamir, Alexander Sax*, William Shen*, Leonidas Guibas, Jitendra Malik, Silvio Savarese.

TASK BANK DATASET
The taskbank folder contains information about our pretrained models, and scripts to download them. There are sample outputs, and links to live demos. The data folder contains information and statistics about the dataset, some sample data, and instructions for how to download the full dataset.
models cauthron
Task affinity analyses and results Website
This folder contains the raw and normalized data used for measuring task affinities. The webpage of the project with links to assets and demos.
task affinity analyses and results Website front page

Citation

If you find the code, models, or data useful, please cite this paper:

@inproceedings{zamir2018taskonomy,
  title={Taskonomy: Disentangling Task Transfer Learning},
  author={Zamir, Amir R and Sax, Alexander and and Shen, William B and Guibas, Leonidas and Malik, Jitendra and Savarese, Silvio},
  booktitle={2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2018},
  organization={IEEE}
}
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