Bibliographic record
Abstract
Few-shot Learning (FSL) approaches aim to develop generalizable models that can classify novel data points with a small set of labeled training data for each class. FSL approaches have the potential to narrow down the performance gap between machines and humans, but it is challenging because humans can quickly adapt to new activities and make decisions based on organized and reusable concepts. However, existing FSL approaches learn complex feature representations ignoring the conceptual information. We propose an Attentional Feature Fusion for Few-shot Learning (AF3), a semi-supervised approach that combines features of multiple scales and utilizes prior knowledge to learn better human interpretable concepts. Attentional feature fusion involves merging features from various layers and branches through an attention mechanism that prioritizes different features through attentional weights. AF3 extracts more discriminative features by generating attention maps for both query and support images. We evaluated our AF3 model in FSL settings on three benchmark datasets, including a fine-grained image classification. Extensive experiments show that our AF3 model outperformed the state-of-the-art in the most challenging 5-way-5-shot learning tasks.
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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 | 0.000 | 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".