Visual cortex on the GPU: Biologically inspired classifier and feature descriptor for rapid recognition
Bibliographic record
Abstract
We present a biologically motivated classifier and feature descriptors that are designed for execution on Single Instruction Multi Data hardware and are applied to high speed multiclass object recognition. Our feature extractor uses a cellular tuning approach to select the optimal Gabor filters to process a given input, followed by the computation of scale and rotation-invariant features that are sparsified with a lateral inhibition mechanism. Neighboring features are pooled into feature hierarchies whose resonant properties are used to select the most representative hierarchies for each object class. The feature hierarchies are used to train a novel form of Adaptive Resonance Theory classifier for multiclass object recognition. Our model has unprecedented biologically plausibility at all stages and uses the programmable Graphics Processing Unit (GPU) for high speed feature extraction and object classification. We demonstrate the speedup achieved with the use of the GPU and test our model on the Caltech 101 and 15 Scene datasets, where our system achieves state-of-the-art performance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".