Use of off-line dynamic programming for efficient image interpretation
Bibliographic record
Abstract
An interpretation system finds the likely mappings from portions of an image to real-world objects. An interpretation policy specifies when to apply which imaging operator, to which portion of the image, during every stage of interpretation. Earlier results compared a number of policies, and demonstrated that policies that select operators which maximize the information gain per cost, worked most effectively. However, those policies are myopic — they rank the operators based only on their immediate rewards. This can lead to inferior overall results: it may be better to use a relatively expensive operator first, if that operator provides information that will significantly reduce the cost of the subsequent operators. This suggests using some lookahead process to compute the quality for operators non-myopically. Unfortunately, this is prohibitively expensive for most domains, especially for domains that have a large number of complex states. We therefore use ideas from reinforcement learning to compute the utility of each operator sequence. In particular, our simplifications of interpretation states, to precompute the utility of each relevant sequence. It does this off-line, over a training sample of images. At run time, our interpretation system uses these estimates to decide when to use which imaging operator. Our empirical results, in the challenging realworld domain of face recognition, demonstrate that this approach works more effectively than myopic approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".