CoNAL

crowdkit.learning.conal.CoNAL | Source code

CoNAL(
    self,
    num_labels: int,
    n_workers: int,
    com_emb_size: int = 20,
    user_feature: Optional[...] = None
)
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Common Noise Adaptation Layers (CoNAL). This method introduces two types of confusions: worker-specific and

global. Each is parameterized by a confusion matrix. The ratio of the two confusions is determined by the common noise adaptation layer. The common noise adaptation layer is a trainable function that takes the instance embedding and the worker ID as input and outputs a scalar value between 0 and 1.

Zhendong Chu, Jing Ma, and Hongning Wang. Learning from Crowds by Modeling Common Confusions.

Proceedings of the AAAI Conference on Artificial Intelligence, 35(7), 5832-5840, 2021.

https://doi.org/10.1609/aaai.v35i7.16730

Examples:

from crowdkit.learning import CoNAL
import torch
input = torch.randn(3, 5)
workers = torch.tensor([0, 1, 0])
embeddings = torch.randn(3, 5)
conal = CoNAL(5, 2)
conal(embeddings, input, workers)
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Methods summary

Last updated: March 31, 2023