<i>AR</i><SUP align="right">2</SUP><i>PNET</i>: an adversarially robust re-weighting prototypical network for few-shot learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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