Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR)\n Approach to Understanding Deep Neural Networks
Bibliographic record
Abstract
In this work, we propose CLass-Enhanced Attentive Response (CLEAR): an\napproach to visualize and understand the decisions made by deep neural networks\n(DNNs) given a specific input. CLEAR facilitates the visualization of attentive\nregions and levels of interest of DNNs during the decision-making process. It\nalso enables the visualization of the most dominant classes associated with\nthese attentive regions of interest. As such, CLEAR can mitigate some of the\nshortcomings of heatmap-based methods associated with decision ambiguity, and\nallows for better insights into the decision-making process of DNNs.\nQuantitative and qualitative experiments across three different datasets\ndemonstrate the efficacy of CLEAR for gaining a better understanding of the\ninner workings of DNNs during the decision-making process.\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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".