A Universal Representation Transformer Layer for Few-Shot Image Classification
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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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".