Learning Rotational Invariant Dictionary for Sparse Coding based Key-point Detection
Bibliographic record
Abstract
Recent research has shown the effectiveness of a Scale and Rotational Invariant Sparse Coding based Key-point Detector. However, this detector utilizes a dictionary generated by combining a carefully selected seed dictionary with multiple rotated versions of its atoms. This manual generation process is time-demanding, thus a novel autoencoder structure for automating it is proposed. Specifically, the weights between input and hidden layers of the structure are designed to embed a rotational invariant dictionary. Indeed, it is a duplet structure constrained by a novel loss function such that rotated inputs correspond to circularly shifted versions of hidden layer responses. To our knowledge, an autoencoder embedded in its weight a rotational invariant dictionary is new and the proposed loss function is the first to fulfill this requirement. Experimental results show that the learned dictionary preserves the original detector's performance, helps achieve state-of-the-art key-point detection performance while substantially eliminating the laborious work.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".