Skip to content

Code for the paper CVPR‘18 "A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts"

Notifications You must be signed in to change notification settings

EthanZhu90/ZSL_GAN

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

94 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

ZSL_GAN

code for the conference and the journal versions of the paper:

Yizhe Zhu, Mohamed Elhoseiny, Bingchen Liu, Xi Peng and Ahmed Elgammal "A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts", CVPR, 2018

Processed feature Data:

You can download the dataset CUBird and NABird
Put the uncompressed data to the folder "data"

Raw wikipedia article data:

Raw wikipedia article data of CUBird and NABird, as well as detailed merging information of NABird, can be obtained here.

Reproduce results

CUBird SCS mode && SCE mode

python train_CUB.py --splitmode easy
python train_CUB.py --splitmode hard

NABird SCS mode && SCE mode

python train_NAB.py --splitmode easy
python train_NAB.py --splitmode hard

Results evaluated on GBU setting[1]

Download the data and uncompress it to the folder 'data/GBU'.

python train_GBU.py --dataset CUB1 --preprocessing --z_dim 100
python train_GBU.py --dataset AWA1 --preprocessing --z_dim 10
python train_GBU.py --dataset AWA2 --preprocessing --z_dim 10
python train_GBU.py --dataset APY --preprocessing --z_dim 10
python train_GBU.py --dataset SUN --preprocessing --z_dim 10
python train_GBU.py --dataset FLO --preprocessing --z_dim 100
Model AwA1 T1 u s H CUB T1 u s H
DAP [2] 44.1 0.0 88.7 0.0 40.0 1.7 67.9 3.3
CONSE [3] 45.6 0.4 88.6 0.8 34.3 1.6 72.2 3.1
SSE [4] 60.1 7.0 80.5 12.9 43.9 8.5 46.9 14.4
DEVISE [5] 54.2 13.4 68.7 22.4 52.0 23.8 53.0 32.8
SJE [6] 65.6 11.3 74.6 19.6 53.9 23.5 59.2 33.6
LATEM [7] 55.1 7.3 71.7 13.3 49.3 15.2 57.3 24.0
ESZSL [8] 58.2 6.6 75.6 12.1 53.9 12.6 63.8 21.0
ALE [9] 59.9 16.8 76.1 27.5 54.9 23.7 62.8 34.4
SYNC [10] 54.0 8.9 87.3 16.2 55.6 11.5 70.9 19.8
SAE [11] 53.0 1.8 77.1 3.5 33.3 7.8 54.0 13.6
DEM [12] 68.4 32.8 84.7 47.3 51.7 19.6 57.9 29.2
GAZSL (OURS) 68.2 29.6 84.2 43.8 55.8 31.68 61.34 41.78
Model AwA2 T1 u s H aPY T1 u s H
DAP [2] 46.1 0.0 84.7 0.0 33.8 4.8 78.3 9.0
CONSE [3] 44.5 0.5 90.6 1.0 26.9 0.0 91.2 0.0
SSE [4] 61.0 8.1 82.5 14.8 34.0 0.2 78.9 0.4
DEVISE [5] 59.7 17.1 74.7 27.8 39.8 4.9 76.9 9.2
SJE [6] 61.9 8.0 73.9 14.4 32.9 3.7 55.7 6.9
LATEM [7] 55.8 11.5 77.3 20.0 35.2 0.1 73.0 0.2
ESZSL [8] 58.6 5.9 77.8 11.0 38.3 2.4 70.1 4.6
ALE [9] 62.5 14.0 81.8 23.9 39.7 4.6 73.7 8.7
SYNC [10] 46.6 10.0 90.5 18.0 23.9 7.4 66.3 13.3
SAE [11] 54.1 1.1 82.2 2.2 8.3 0.4 80.9 0.9
DEM [12] 67.1 30.5 86.4 **45.1 35.0 11.1 75.1 19.4
GAZSL (OURS) 70.2 35.4 86.9 50.3 41.13 14.17 78.63 24.01
Model SUN T1 u s H FLO T1 u s H
DAP [2] 39.9 4.2 25.1 7.2
CONSE [3] 38.8 6.8 39.9 11.6
SSE [4] 51.5 2.1 36.4 4.0
DEVISE [5] 56.5 16.9 27.4 20.9
SJE [6] 53.7 14.7 30.5 19.8
LATEM [7] 55.3 14.7 28.8 19.5
ESZSL [8] 54.5 11.0 27.9 15.8
ALE [9] 58.1 21.8 33.1 26.3
SYNC [10] 56.3 7.9 43.3 13.4
SAE [11] 40.3 8.8 18.0 11.8 
DEM [12] 61.9 20.5 34.3 25.6
GAZSL (OURS) 61.3 22.1 39.3 28.3 60.5 28.1 77.4 41.2

Note: The results of work [2-11] are copied from TABLE6 in [1]. The results of work [12] are obtained from the authors' report. Thanks to Li Zhang for the template of README.

Citation

If you find this implementation or the analysis conducted in our report helpful, please consider citing:

@inproceedings{Yizhe_ZSL_2018,  
    Author = {Yizhe Zhu, Mohamed Elhoseiny, Bingchen Liu, Xi Peng and Ahmed Elgammal},  
    Title = {A Generative Adversarial  Approach for Zero-Shot Learning from Noisy Texts},  
    Booktitle = {CVPR},  
    Year = {2018}  
}

TODO:

Update:

  • 2022/01/10 update the link of shared raw wiki data
  • 2018/10/22 add AUC Score for CUBird and NABird
  • 2018/07/23 add results on GBU setting
  • 2018/07/18 add the experiments of NABird.
  • 2018/07/18 merge the train.py and test.py to one file
  • 2018/07/17 make code compatable with python2&3

References

About

Code for the paper CVPR‘18 "A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts"

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages