<i>AR</i><SUP align="right">2</SUP><i>PNET</i>: an adversarially robust re-weighting prototypical network for few-shot learning
Bibliographic record
Abstract
Robust re-weighting prototypical networks (RRPNet) model is a promising method to improve the robustness of prototypical networks (ProtoNet). However, the performance of RRPNet is limited when the examples are scare and the noise is trivial. In this paper we propose a novel re-weighting prototypical networks framework for few-shot learning based on AT, called AR2PNet, to enhance the performance of RRPNet. Specifically, instead of directly calculating the similarity between the naive representations of the examples, we calculate such similarity between prototype representations, which is conductive to reducing the computation cost as well as enhancing the model prediction accuracy. Meanwhile, to encourage the model to resist adversarial examples, we formulate the loss function as a minimax problem inspired by the conception of AT. We conduct experiments on CIFAR-FS and MiniImageNet dataset, and the experimental results demonstrate the effectiveness of the propose method.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".