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ZSL-KG is a general-purpose zero-shot learning framework with a novel transformer graph convolutional network (TrGCN) to learn class representation from common sense knowledge graphs.

Build Status

Reference paper: Zero-shot Learning with Common Sense Knowledge graphs.

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Performance ZSL-KG compared to other existing graph-based zero-shot learning frameworks.

Method Ontonotes (Strict) BBN (Strict) SNIPS-NLU (Acc.) AWA2 (H) aPY (H) ImageNet (All T-1) Avg.
GCNZ 41.5 21.5 82.5 73.3 58.1 1.0 46.3
SGCN 42.6 24.9 50.3 73.7 56.8 1.5 41.6
DGP 41.1 24.0 64.4 75.1 55.7 1.4 43.6
ZSL-KG 45.2 26.7 89.0 74.6 61.6 1.7 49.8

ZSL-KG outperforms existing graph-based frameworks on five out of six benchmark datasets.

For more details on the experiments, refer to nayak-tmlr22-code.


The package requires python >= 3.7. To install the package, type the following command:

pip install .

Example Usage

In our framework, we use AutoGNN to easily create graph neural networks for zero-shot learning.

from zsl_kg.class_encoder.auto_gnn import AutoGNN
from zsl_kg.common.graph import NeighSampler

trgcn = {
    "input_dim": 300,
    "output_dim": 2049,
    "type": "trgcn",
    "gnn": [
            "input_dim": 300,
            "output_dim": 2048,
            "activation": nn.LeakyReLU(0.2),
            "normalize": True,
            "sampler": NeighSampler(100, mode="topk"),
            "fh": 100,
            "input_dim": 2048,
            "output_dim": 2049,
            "activation": None,
            "normalize": True,
            "sampler": NeighSampler(50, mode="topk"),

class_encoder = AutoGNN(trgcn)

Our framework supports the following graph neural networks: gcn, gat, rgcn, lstm, trgcn. You can change the type to any of the available to graph neural networks to instantly create a new graph neural network.

For more examples, refer to nayak-tmlr22-code.

Run Tests

To run the tests, please type the following command:



Please cite the following paper if you are using our framework.

  Author = {Nayak, Nihal V. and Bach, Stephen H.},
  Title = {Zero-Shot Learning with Common Sense Knowledge Graphs},
  Journal = {Transactions on Machine Learning Research (TMLR)},
  Year = {2022}}