Meta-Learning with Adaptive Hyperparameters
Authors: Sungyong Baik, Myungsub Choi, Janghoon Choi, Heewon Kim, Kyoung Mu Lee
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we demonstrate the effectiveness of our proposed weight-update rule (ALFA) in few-shot learning. The experimental results validate that the Adaptive Learning of hyperparameters for Fast Adaptation (ALFA) is the equally important ingredient that was often neglected in the recent few-shot learning approaches. |
| Researcher Affiliation | Academia | Sungyong Baik Myungsub Choi Janghoon Choi Heewon Kim Kyoung Mu Lee ASRI, Department of ECE, Seoul National University {dsybaik, cms6539, ultio791, ghimhw, kyoungmu}@snu.ac.kr |
| Pseudocode | Yes | Algorithm 1 Adaptive Learning of Hyperparameters for Fast Adaptation (ALFA) |
| Open Source Code | Yes | 1The code is available at https://github.com/baiksung/ALFA |
| Open Datasets | Yes | For few-shot classification, we use the two most popular datasets: mini Image Net [38] and tiered Image Net [29]. CUB-200-2011 (denoted as CUB) [40], Triantafillou et al. [37] recently introduced a large-scale dataset, named Meta-Dataset |
| Dataset Splits | Yes | mini Image Net...is divided into 3 subsets of classes without overlap: 64 classes for meta-train set, 16 for meta-validation set, and 20 for meta-test set as in [28]. Similarly, tiered Image Net is composed of 608 classes...where 20 / 6 / 8 disjoint categories are used as meta-train / meta-validation / meta-test sets, respectively. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper does not provide specific version numbers for software components or libraries used (e.g., "Python 3.8", "PyTorch 1.9"). |
| Experiment Setup | Yes | In the meta-training stage, the meta-learner gφ is trained over 100 epochs (each epoch with 500 iterations) with a batch size of 2 and 4 for 5-shot and 1-shot, respectively. At each iteration, we sample N classes for N-way classification, followed by sampling k labeled examples for Di and 15 examples for D i for each class. |