Improving Task-Specific Generalization in Few-Shot Learning via Adaptive Vicinal Risk Minimization
Authors: Long-Kai Huang, Ying Wei
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 hlongkai@gmail.com Ying Wei City University of Hong Kong yingwei@cityu.edu.hk |
| 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. |