E-MART: An Improved Misclassification Aware Adversarial Training with Entropy-Based Uncertainty Measure
Bibliographic record
Abstract
Recently, adversarial training (AT) has been demonstrated to be effective to improve deep neural network (DNN) robustness against adversarial examples. Among them, Misclassification Aware adveRsarial Training (MART) is the most promising one, which incorporates an explicit differentiation of misclassified examples as a regularizer. However, MART uses prediction error for identifying the misclassified examples, and yet it fails to achieve the greatest performance. This crux lies in the fact that the prediction error only focuses on the output probability regarding with the ground-truth label, neglecting the impact of the complement classes. In this paper, we offer a unique insight into the condition that emphasizes learning on misclassified examples, and propose an improved MART method with entropy-based uncertainty measure (termed as E-MART). Specifically, we consider the impact of the outputs from all classes and develop an entropy-based uncertainty measure (EUM) to provide reliable evidence that indicates the impact of the misclassified and correctly classified examples. Moreover, based on EUM, we conduct a soft decision scheme to optimize the loss function of AT, which help to make the efficient training for the model's final robustness. We have carried out experiments on CIFAR-10 dataset, and the experimental results demonstrate the effectiveness of our method.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.002 |
| 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".