Estimating Annotator- and Instance-dependent Noise Transition Matrices for Learning from Crowdsourced Data

Grunnleggende konsepter

The core message of this paper is to propose a method for estimating general annotator- and instance-dependent noise transition matrices in the learning from crowds setting, where the annotations are obtained through crowdsourcing services and exhibit label noise that depends on both the annotator and the instance.

Sammendrag

The paper addresses the problem of learning from crowds, where the training data annotations are obtained through crowdsourcing services. In this setting, the label noise is both annotator-dependent and instance-dependent, making it challenging to model the noise generation process accurately.
The key highlights and insights are:

The authors parameterize the annotator- and instance-dependent transition matrices using deep neural networks to maintain modeling generality, in contrast to prior works that simplify the problem by assuming instance-independent or using simple parametric models.

To alleviate the modeling challenge caused by sparse annotations from individual annotators, the authors propose to perform knowledge transfer. They first model the mixture of noise patterns across all annotators, and then transfer this global knowledge to individual annotators.

Furthermore, to address the issue that the transfer from the global mixture to individuals may cause annotators with highly different noise patterns to perturb each other, the authors employ knowledge transfer between identified neighboring annotators to calibrate the individual transition matrix estimations.

Theoretical analyses are provided to justify the role of the proposed knowledge transfer approaches in addressing the challenges of modeling general annotator- and instance-dependent transition matrices.

Experiments on synthetic and real-world crowdsourcing datasets demonstrate the superiority of the proposed method compared to state-of-the-art baselines.

Statistikk

The noise rate in the synthetic datasets ranges from 10% to 50%.
Each instance has on average 2 noisy labels from randomly selected annotators.

Sitater

"Without losing modeling generality, we parameterize AIDTM with deep neural networks."
"To alleviate the modeling challenge caused by annotation sparsity, we assume that each annotator shares its noise pattern with similar annotators, and propose to perform knowledge transfer to achieve estimating general AIDTM by deep networks."
"We provide the theoretical analyses to justify the role of knowledge transfer, which shows that the knowledge transfer from global to individuals addresses the challenge that sparse individual annotations cannot train a high-complexity neural network."

Viktige innsikter hentet fra

Transferring Annotator- and Instance-dependent Transition Matrix for Learning from Crowds

by Shikun Li,Xi... klokken arxiv.org 04-16-2024

https://arxiv.org/pdf/2306.03116.pdf

Dypere Spørsmål

How can the proposed method be extended to handle cases where the number of annotations per instance varies

To extend the proposed method to handle cases where the number of annotations per instance varies, we can make the following adjustments:

Dynamic Graph Construction: Instead of assuming a fixed number of neighbors for each annotator, we can dynamically construct the similarity graph based on the varying number of annotations per instance. This way, the knowledge transfer can adapt to different annotation densities.

Adaptive Knowledge Transfer: Implement an adaptive knowledge transfer mechanism that can adjust the amount of information transferred between annotators based on the number of annotations they provide. Annotators with more annotations can contribute more to the knowledge transfer process.

Variable Batch Processing: Modify the training process to handle varying numbers of annotations per instance in each batch. This can involve dynamically adjusting the batch size or implementing padding techniques to ensure consistency in training.

By incorporating these modifications, the proposed method can effectively handle cases where the number of annotations per instance varies, ensuring robustness and adaptability in learning from crowds.

What are the potential limitations of the knowledge transfer approach, and how can they be addressed

The knowledge transfer approach in the proposed method may have some limitations, which can be addressed through the following strategies:

Overfitting: One potential limitation is the risk of overfitting when transferring knowledge between annotators, especially in cases where the noise patterns are highly diverse. To address this, regularization techniques such as dropout or weight decay can be applied during the knowledge transfer process.

Limited Generalization: Another limitation could be the limited generalization of the transferred knowledge to unseen data. To mitigate this, techniques like data augmentation or incorporating diverse training examples can help enhance the generalization capability of the model.

Complexity: The complexity of the knowledge transfer process may increase with a large number of annotators or instances. Implementing efficient algorithms for graph construction and GCN-based mapping can help manage the computational complexity and ensure scalability.

By addressing these potential limitations through appropriate regularization, generalization strategies, and efficient algorithms, the knowledge transfer approach can be optimized for improved performance and robustness.

How can the insights from this work on modeling annotator- and instance-dependent noise be applied to other machine learning problems beyond learning from crowds

The insights gained from modeling annotator- and instance-dependent noise in learning from crowds can be applied to other machine learning problems beyond this specific domain. Here are some ways these insights can be leveraged:

Semi-Supervised Learning: The concept of modeling noise patterns and leveraging knowledge transfer can be beneficial in semi-supervised learning scenarios where labeled data may contain noise. By incorporating similar techniques, models can effectively learn from noisy labeled data and improve performance.

Anomaly Detection: In anomaly detection tasks, where noise or outliers can impact model performance, understanding and modeling different noise patterns can enhance the detection of anomalies. Knowledge transfer mechanisms can help in capturing diverse noise patterns and improving anomaly detection accuracy.

Natural Language Processing: In NLP tasks, such as sentiment analysis or text classification, where annotations may vary in quality or reliability, modeling annotator-dependent noise can aid in building more robust models. By transferring knowledge between similar annotators, the models can better handle noisy annotations and improve performance.

By applying the insights from modeling annotator- and instance-dependent noise to these and other machine learning problems, we can enhance model robustness, accuracy, and generalization capabilities across various domains.