Code and Data for the paper: "OntoZSL: Ontology-enhanced Zero-shot Learning". In this work, we propose to utilize ontology and generative adversarial network to deal with the zero-shot learning problems in image classification and KG completion.
| Dataset | # Classes (Total/Seen/Unseen) | # Ontology Schema (Triples/Concepts/Properties) |
|---|---|---|
| AwA | 50/40/10 | 1,256/180/12 |
| ImNet-A | 80/28/52 | 563/227/19 |
| ImNet-O | 35/10/25 | 222/115/8 |
| Dataset | # Relations (Total/Train/Val/Test) | # Ontology Schema (Triples/Concepts/Properties) |
|---|---|---|
| NELL-ZS | 139/10/32 | 3,055/1,186/4 |
| Wikidata-ZS | 469/20/48 | 10,399/3,491/8 |
python 3.5PyTorch >= 1.0.0
You need to download pretrained Glove word embedding dictionary, uncompress it and put all files to the folder data/glove/.
Download public image features and dataset split for AwA2, uncompress it and put the files in AWA2 folder to our folder data/AwA2/.
Download the image features and the word embeddings of ImageNet classes as well as their splits from here and put them to the folder data/ImageNet/.
You can download these two datasets from here and put them to the corresponding data folder.
The first thing you need to do is to train the text-aware ontology encoder using the code in the folder code/OntoEncoder, you can get more details at code/OntoEncoder/README.md.
Secondly, with well-trained ontology embedding, you can take it as the input of generative model, see the codes in the folders code/IMGC and code/KGC. The running commands are listed in the corresponding README.md files.
Note: you can skip the first step if you just want to use the ontology embedding we learned, the files are provided in the corresponding directories.