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.

Related tags

Deep Learningmachine-learningknowledge-graphzero-shot-learning
Overview

ZSL-KG

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

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-arxiv20-code.

Installation

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-arxiv20-code.

Run Tests

To run the tests, please type the following command:

pytest

Citation

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

@article{nayak:arxiv20,
  Author = {Nayak, Nihal V. and Bach, Stephen H.},
  Title = {Zero-Shot Learning with Common Sense Knowledge Graphs},
  Volume = {arXiv:2006.10713 [cs.LG]},
  Year = {2020}}
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Comments
  • Graph usage query in object classification AWA2

    Graph usage query in object classification AWA2

    Hi, I'm not entirely sure if this is where I should be asking this but I was wondering if you could help me clarify a question.

    1 When performing zero shot object classification in AWA2, what is the graph that is used? (I assume a knowledge graph such as ConceptNet?)

    2 If this is a knowledge graph, than what is the "input" to the trgcn? As in where are these "concepts" coming from, is it from the class attributes?

    opened by shadowbourne 2
  • Quick note

    Quick note

    Dear Team behind @zsl-kg,

    This framework is really cool, however, I was quite disappointed to see you are re-writing everything from scratch when you could have used an awesome framework like PyTorch Geometric.

    Best regards, Thomas Chaton

    opened by tchaton 1
Releases(v0.0.1)
Owner
Bats Research
Bats Research
GitHub Repository https://arxiv.org/abs/2006.10713
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