Perceptual Quality-based Model Training under Annotator Label Uncertainty

Core Concepts

Training models with multiple labels based on perceptual quality enhances reliability and generalizability.

Abstract

Annotator label uncertainty affects model reliability, leading to degradation in generalizability and prediction uncertainty. Existing algorithms struggle with this uncertainty. A novel framework uses perceptual quality to generate multiple labels for training, improving model performance without massive annotations. The method selects samples with low quality scores, assigns de-aggregated labels objectively, and demonstrates enhanced generalizability and prediction accuracy. The study highlights the impact of annotator disagreement across various fields like image classification, medical diagnosis, and seismic interpretation.

Stats

Pages={965–969}
DOI = {10.1190/image2023-3916384.1}
arXiv:2403.10190v1 [cs.CV] 15 Mar 2024

Quotes

"Annotators exhibit disagreement during data labeling, which can be termed as annotator label uncertainty."
"Training with a single low-quality annotation per sample induces model reliability degradations."
"Our experiments demonstrate that training with the proposed framework alleviates the degradation of generalizability and prediction uncertainty caused by annotator label uncertainty."

Key Insights Distilled From

Perceptual Quality-based Model Training under Annotator Label Uncertainty

by Chen Zhou,Mo... at arxiv.org 03-18-2024

https://arxiv.org/pdf/2403.10190.pdf

Deeper Inquiries

How can the concept of perceptual quality-based multi-label training be applied in other domains beyond geophysics?

The concept of perceptual quality-based multi-label training, as demonstrated in the context of addressing annotator label uncertainty in geophysics, can be extended to various other domains. In fields such as image classification, medical diagnosis, and natural language processing, where human annotations may exhibit disagreement leading to label uncertainty, this approach can prove beneficial. For instance:

Image Classification: Utilizing perceptual quality assessments to objectively generate multiple labels for ambiguous or challenging images can enhance model reliability by providing diverse perspectives on labeling.
Medical Diagnosis: In scenarios where different experts provide varying diagnoses for a particular case, incorporating a multi-label framework based on perceptual quality assessments could lead to more robust models that consider diverse diagnostic possibilities.
Natural Language Processing: When dealing with text data annotated by multiple individuals with differing interpretations or classifications, applying a similar approach could help capture the nuances and uncertainties present in the annotations.
By leveraging perceptual quality assessments to identify samples with high annotator label uncertainty across these domains and assigning multiple labels based on objective criteria, models trained using this framework are likely to exhibit improved generalizability and prediction certainty.

What are the potential drawbacks or limitations of relying solely on perceptual quality assessments for model training?

While utilizing perceptual quality assessments for model training offers several advantages in mitigating annotator label uncertainty, there are some potential drawbacks and limitations to consider:

Subjectivity: Perceptual quality assessments rely on human judgment and perception. Different assessors may have varying opinions on what constitutes low-quality samples, introducing subjectivity into the process.
Scalability: Conducting detailed perceptual evaluations for large datasets can be time-consuming and resource-intensive. Scaling up such an approach may pose challenges when dealing with massive amounts of data.
Complexity: The algorithms used for assessing perceptual quality need to be carefully designed and validated to ensure their effectiveness across different types of data and annotation tasks.
Interpretation Issues: Interpreting results from complex perceptual assessment algorithms might require domain expertise or additional validation steps to ensure their relevance in improving model performance.

Therefore, while valuable insights can be gained from incorporating perceptual quality assessments into model training pipelines, it is essential to balance their use with other techniques and considerations to address these limitations effectively.

How might advancements in uncertainty estimation algorithms impact the effectiveness of addressing annotator label uncertainty?

Advancements in uncertainty estimation algorithms play a crucial role in enhancing the efficacy of addressing annotator label uncertainty:

Improved Model Robustness - Advanced uncertainty estimation methods like Monte Carlo Dropout or deterministic quantification techniques enable models to better quantify their confidence levels during predictions even when trained with noisy labels due to annotator disagreements.
Better Generalization - State-of-the-art uncertainty estimation algorithms help models generalize well beyond their training data distribution by capturing epistemic uncertainties arising from limited labeled examples or conflicting annotations.
Enhanced Decision-Making - By accurately estimating predictive uncertainties under conditions of annotator label ambiguity, models equipped with advanced uncertainty quantification mechanisms make more informed decisions while acknowledging potential errors introduced by noisy annotations.

As these algorithms evolve towards handling complex sources of uncertainties inherent in real-world datasets affected by annotator discrepancies (such as those seen across various applications), they contribute significantly towards building reliable machine learning systems that are resilient against imperfect labeling practices commonly encountered during data annotation processes involving human judgments