Diminishing Simplicity Bias using GAN Generated UnbiasedAugmentations
Bibliographic record
Abstract
Simplicity Bias (SB) characterizes the propensity of neural networks (NN) to excessively favor simpler features, while neglecting potentially informative complex features. This bias hinders a network's capacity to generalize effectively, contributing to reduced robustness outside of distributions (OOD). The challenge arises as more intricate features are prone to being overshadowed by simpler features, leading to feature selection bias and spurious correlation. These spurious correlations can give an inflated view of the model's performance on a test set is good, but the model may be sub-optimal and non-robust because of its over reliance on simple features. In this paper, we investigate the adverse effects of SB on both robustness and model generalization. To address this issue, we introduce a Generative Adversarial Network (GAN) designed to generate unbiased examples by deceiving the original NN exhibiting SB. We demonstrate that the incorporation of these generated unbiased examples encourages learning from complex features previously overlooked by the biased model. Our approach hinges on leveraging models exhibiting SB, and we substantiate its efficacy through controlled datasets showcasing SB. We used four datasets in this paper and show that our approach is effective in mitigating SB across all four datasets.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".