Label noise learning with the combination of CausalNL and CGAN models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Since Deep Neural Networks easily overfit label errors, which will degenerate the performance of Deep Learning algorithms, recent research gives a lot of methodology for this problem. A recent model, causalNL, uses a structural causalNL model for instance-dependent label-noise learning and obtained excellent experimental results. The implementation of the algorithm is based on the VAE model, which encodes latent variables Y and Z with the observable variables X and Y. This in turn generates the transfer matrix. But it relies on some unreasonable assumptions. In this paper, we introduce CGAN to the causalNL model, which avoids setting P(Y) and P(Z) for a specific distribution. GAN’s ability of processing data do not need to set a specific distribution. ICC was validated on several authoritative datasets and compared to a variety of proven algorithms including causalNL. The paper presents notable findings on the ICC model (Introduce CGAN to causalNL) shows excellent training ability on most datasets. Surprisingly, ICC shows totally higher accuracy than causalNL in CIFAR10.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it