A Universal Representation Transformer Layer for Few-Shot Image Classification
Why this work is in the frame
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Bibliographic record
Abstract
Few-shot classification aims to recognize unseen classes when presented with\nonly a small number of samples. We consider the problem of multi-domain\nfew-shot image classification, where unseen classes and examples come from\ndiverse data sources. This problem has seen growing interest and has inspired\nthe development of benchmarks such as Meta-Dataset. A key challenge in this\nmulti-domain setting is to effectively integrate the feature representations\nfrom the diverse set of training domains. Here, we propose a Universal\nRepresentation Transformer (URT) layer, that meta-learns to leverage universal\nfeatures for few-shot classification by dynamically re-weighting and composing\nthe most appropriate domain-specific representations. In experiments, we show\nthat URT sets a new state-of-the-art result on Meta-Dataset. Specifically, it\nachieves top-performance on the highest number of data sources compared to\ncompeting methods. We analyze variants of URT and present a visualization of\nthe attention score heatmaps that sheds light on how the model performs\ncross-domain generalization. Our code is available at\nhttps://github.com/liulu112601/URT.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it