LiangjunFeng / denmo

this is a demo

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TransferIncrement_ZSL

Source code of Transfer Increment for Generalized Zero-Shot Learning In TNNLS 2021 The details of model can be found in
L. J. Feng, et al. Transfer Increment for Generalized Zero-Shot Learning, TNNLS, 2021.

Download the Dataset:

Link: https://pan.baidu.com/s/12SGtog43jJ0cZRpGOqTgxg
code: vevv

Fast execution in command line:

pip3 install cvxpy
python3 AWA2_PA.py

Results Example:

============================Experiment: Ours+IOM+Res.============================ (24270, 2048) (24270,) (24270, 85) (6985, 2048) (6985,) (6985, 85) (6067, 2048) (6067,) (6067, 85) Base training for 85 attribute classifiers begin... 10 th classifier is training 20 th classifier is training 30 th classifier is training 40 th classifier is training 50 th classifier is training 60 th classifier is training 70 th classifier is training 80 th classifier is training 85 th classifier is training Base training for 85 attribute classifiers is over! Incremental training begin... Incremental samples are 1, S is 0.8757, U is 0.1907, H is 0.3132, training time is 10.897. Incremental samples are 3, S is 0.8698, U is 0.2261, H is 0.3588, training time is 10.6462. Incremental samples are 5, S is 0.8584, U is 0.3173, H is 0.4633, training time is 10.3452. Incremental samples are 7, S is 0.8121, U is 0.3812, H is 0.5189, training time is 10.7549. Incremental samples are 9, S is 0.8142, U is 0.4212, H is 0.5552, training time is 10.0988. Incremental samples are 10, S is 0.8388, U is 0.4467, H is 0.5829, training time is 11.7884. Incremental samples are 13, S is 0.8108, U is 0.4422, H is 0.5723, training time is 10.9359. Incremental samples are 15, S is 0.7872, U is 0.48, H is 0.5964, training time is 9.9649. Incremental samples are 17, S is 0.7524, U is 0.5227, H is 0.6169, training time is 10.8031. Incremental samples are 19, S is 0.7898, U is 0.5162, H is 0.6244, training time is 10.2848. Incremental samples are 20, S is 0.7391, U is 0.5609, H is 0.6378, training time is 10.1297. Incremental samples are 21, S is 0.7551, U is 0.5288, H is 0.622, training time is 9.6607. Incremental samples are 23, S is 0.7646, U is 0.5658, H is 0.6503, training time is 9.9159. Incremental samples are 25, S is 0.7493, U is 0.5684, H is 0.6464, training time is 9.9403. Incremental samples are 27, S is 0.7242, U is 0.6089, H is 0.6616, training time is 10.0022
Incremental samples are 29, S is 0.6816, U is 0.6429, H is 0.6617, training time is 10.3731. Incremental samples are 30, S is 0.6855, U is 0.5973, H is 0.6384, training time is 10.035. Incremental samples are 31, S is 0.7073, U is 0.591, H is 0.6439, training time is 10.2678. Incremental samples are 33, S is 0.685, U is 0.6462, H is 0.6651, training time is 10.4913. Incremental samples are 35, S is 0.6903, U is 0.5827, H is 0.6319, training time is 10.2225. Incremental samples are 37, S is 0.686, U is 0.6445, H is 0.6646, training time is 10.3919. Incremental samples are 39, S is 0.6718, U is 0.6252, H is 0.6477, training time is 10.5959. Incremental samples are 40, S is 0.673, U is 0.6014, H is 0.6352, training time is 10.1056. Incremental samples are 43, S is 0.6397, U is 0.6736, H is 0.6562, training time is 10.6409. Incremental samples are 45, S is 0.6338, U is 0.6588, H is 0.6461, training time is 10.0113. Incremental samples are 47, S is 0.7005, U is 0.6377, H is 0.6676, training time is 10.2222. Incremental samples are 50, S is 0.66, U is 0.6704, H is 0.6652, training time is 11.9292. Incremental samples are 55, S is 0.6438, U is 0.6474, H is 0.6456, training time is 12.7336. Incremental samples are 60, S is 0.6572, U is 0.674, H is 0.6655, training time is 13.9888.

All rights reserved, citing the following papers are required for reference:

[1] L. J. Feng, et al. Fault Description Based Attribute Transfer for Zero-Sample Industrial Fault Diagnosis, TII, 2021.
[2] L. J. Feng, et al. Transfer Increment for Generalized Zero-Shot Learning, TNNLS, 2021.

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this is a demo

License:GNU General Public License v3.0


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Language:Python 100.0%