HOMRS: High Order Metamorphic Relations Selector for Deep Neural\n Networks
Bibliographic record
Abstract
Deep Neural Networks (DNN) applications are increasingly becoming a part of\nour everyday life, from medical applications to autonomous cars. Traditional\nvalidation of DNN relies on accuracy measures, however, the existence of\nadversarial examples has highlighted the limitations of these accuracy\nmeasures, raising concerns especially when DNN are integrated into\nsafety-critical systems.\n In this paper, we present HOMRS, an approach to boost metamorphic testing by\nautomatically building a small optimized set of high order metamorphic\nrelations from an initial set of elementary metamorphic relations. HOMRS'\nbackbone is a multi-objective search; it exploits ideas drawn from traditional\nsystems testing such as code coverage, test case, path diversity as well as\ninput validation.\n We applied HOMRS to MNIST/LeNet and SVHN/VGG and we report evidence that it\nbuilds a small but effective set of high-order transformations that generalize\nwell to the input data distribution. Moreover, comparing to similar generation\ntechnique such as DeepXplore, we show that our distribution-based approach is\nmore effective, generating valid transformations from an uncertainty\nquantification point of view, while requiring less computation time by\nleveraging the generalization ability of the approach.\n
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".