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New framework identifies noisy labels in datasets using loss aggregation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework called Standardized Loss Aggregation (SLA) to identify noisy labels in large datasets, particularly in medical imaging. SLA quantifies label reliability by aggregating standardized validation losses from repeated cross-validation runs, providing a continuous and interpretable score. This method is more efficient than existing hard-counting approaches, especially in low-noise scenarios, and can help improve dataset quality for various classification tasks. AI

IMPACT Introduces a novel method for improving data quality in AI training, potentially leading to more reliable models.

RANK_REASON The cluster contains an academic paper detailing a new methodology for noisy label detection. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework identifies noisy labels in datasets using loss aggregation

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The cluster contains an academic paper detailing a new methodology for noisy label detection. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Doohyun Park ·

    Task-Agnostic Noisy Label Detection via Standardized Loss Aggregation

    Noisy labels are common in large-scale medical imaging datasets due to inter-observer variability and ambiguous cases. We propose a statistically grounded and task-agnostic framework, Standardized Loss Aggregation (SLA), for detecting noisy labels at the sample level. SLA quantif…

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