AdaptiveDrop: A Simple Adaptive Label Noise Filtering Scheme for Enhanced Self-supervised Speaker Verification
Bibliographic record
Abstract
Using clustering-driven annotations to train a neural network can be a tricky task because of label noise. In this paper, we propose a dynamic and adaptive label noise cleansing method, called AdaptiveDrop which combines both label noise filtering and correction simultaneously in cascade to combine their advantages. Contrary to other label noise filtering approaches, our method filters noisy samples on the fly from an early stage of training. We also provide a variant that incorporates sub-centers per each class for enhanced robustness to label noise by continuously tracking the dominant sub-centers via a dictionary table. AdaptiveDrop is a simple general-purpose method, performed end-to-end in only one stage of training, can be integrated with any loss function, and does not require training from scratch on the cleansed dataset. We show through extensive ablation studies for the self-supervised speaker verification task that our method is effective, benefits from long epochs of iterative filtering and provides consistent performance gains across various loss functions and real-world pseudo-labels.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".