GIST: Generated Inputs Sets Transferability in Deep Learning
Bibliographic record
Abstract
Replication Package for the paper "GIST: Generated Inputs Sets Transferability in Deep Learning" Contains most of the data, models ... used Github link: https://github.com/FlowSs/GIST Part2 of the replication package can be found here: https://zenodo.org/records/10839634 Abstract: To foster the verifiability and testability of Deep Neural Networks (DNN), an increasing number of methodsfor test case generation techniques are being developed. When confronted with testing DNN models, the user can apply any existing test generation technique.However, it needs to do so for each technique and each DNN model under test, which can be expensive.Therefore, a paradigm shift could benefit this testing process: rather than regenerating the test set independentlyfor each DNN model under test, we could transfer from existing DNN models. This paper introduces GIST (Generated Inputs Sets Transferability), a novel approach for the efficienttransfer of test sets. Given a property selected by a user (e.g., neurons covered, faults), GIST enables theselection of good test sets from the point of view of this property among available test sets. This allows theuser to recover similar properties on the transferred test sets as he would have obtained by generating thetest set from scratch with a test cases generation technique. Experimental results show that GIST can selecteffective test sets for the given property to transfer. Moreover, GIST scales better than reapplying test casegeneration techniques from scratch on DNN models under test.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.089 | 0.029 |
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".