Prototype Completion With Knowledge Distillation and Gate Recurrent Unit for Few-Shot Classification
Bibliographic record
Abstract
Few-shot learning(FSL) methods imitate the procedure by which humans learn knowledge from a few samples to identify a novel category. Some FSL methods emphasize obtaining an accurate prototype to precisely measure the similarity with query samples. However, due to the scarcity of data and the noise introduced by background information and other unrelated classes, the prototypes are usually biased and decrease the model’s effectiveness. In this paper, we propose a Knowledge Distillation and GRU-based Prototype Completion Network (KDG-ProComNet) to alleviate the above problem. In KDG-ProComNet, we propose a background discrimination pre-training module to identify the foreground object of samples and use it to obtain more accurate prototypes, thereby reducing the destructive impact of noise information on prototypes. Furthermore, we construct a memory bank to store the prototypes and update them via GRU to rectify biased prototypes and enhance the representativeness of prototypes in the feature space. Extensive experiments are conducted based on miniImageNet and tieredImageNet, and the results demonstrate the outstanding performance of the proposed 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 | 0.002 |
| 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 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".