Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Improving Task-Specific Generalization in Few-Shot Learning via Adaptive Vicinal Risk Minimization
Authors: Long-Kai Huang, Ying Wei
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To verify the performance of the proposed method, we conduct experiments on three standard few-shot learning benchmarks and consolidate the superiority of the proposed method over state-of-the-art few-shot learning baselines. |
| Researcher Affiliation | Collaboration | Long-Kai Huang Tencent AI Lab EMAIL Ying Wei City University of Hong Kong EMAIL |
| Pseudocode | Yes | Algorithm 1 Adaptive Vicinal Few-Shot Learning (ADV) |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] |
| Open Datasets | Yes | We use three benchmarks for performance evaluation: mini Image Net [39], CUB [40] and CIFARFS [2]. |
| Dataset Splits | Yes | The learning rate is determined by performing a grid search from 0.001 to 1 on the tasks constructed by the meta-validation set. |
| Hardware Specification | No | The paper mentions a 'differentiable GPU-based QP solver' but does not specify any exact GPU/CPU models, memory amounts, or detailed computer specifications used for running experiments. |
| Software Dependencies | No | The paper mentions using a 'differentiable GPU-based QP solver [1]' but does not provide specific version numbers for any software components, libraries, or solvers. |
| Experiment Setup | Yes | For ADV-CE, we initialize the weights of the classifier by class prototypes and optimize the vicinal loss in (5) by gradient descent for 100 steps. The learning rate is determined by performing a grid search from 0.001 to 1 on the tasks constructed by the meta-validation set. For ADV-SVM, we solve the QP in (9) by using a differentiable GPU-based QP solver [1]. The regularization parameter λ is set to 0.1 and the parameter σ for RBF kernel is obtained via grid search from 0.1 to 10. In the lazy random walk algorithm, the number of steps T, the lazy stay probability β and the temperature are obtained via grid search in {1, 2, 3, 4, 5}, {0.1, 0.2, 0.5}, {0.01, 0.1, 1, 10}, respectively. |