Meta-Learning Universal Priors Using Non-Injective Change of Variables
Authors: Yilang Zhang, Alireza Sadeghi, Georgios Giannakis
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments conducted on three few-shot learning datasets validate the superiority of data-driven priors over the prespecified ones, showcasing its pronounced effectiveness when dealing with extremely limited data resources. In this section, we test and showcase the empirical superiority of Meta NCo V on both synthetic and real datasets. |
| Researcher Affiliation | Academia | Yilang Zhang Department of ECE University of Minnesota Minneapolis, MN 55414 zhan7453@umn.edu Alireza Sadeghi Department of ECE University of Minnesota Minneapolis, MN 55414 sadeg012@umn.edu Georgios B. Giannakis Department of ECE University of Minnesota Minneapolis, MN 55414 georgios@umn.edu |
| Pseudocode | Yes | Algorithm 1 Meta NCo V algorithm |
| Open Source Code | Yes | Codes for reproducing the results are available at https://github.com/zhangyilang/Meta NCo V. |
| Open Datasets | Yes | Mini Image Net [55] contains 60, 000 images sampled from the full Image Net (ILSVRC-12) dataset... Tiered Image Net [42] is a larger subset of the Image Net dataset... CUB-200-2011 [57] is an extended version of the Caltech-UCSD Birds(CUB)-200 dataset... |
| Dataset Splits | Yes | The dataset is divided into a training subset Dtrn t Dt, and a validation subset Dval t := Dt \ Dtrn t. In the experiments, we adopt the dataset split suggested by [41], where 64, 16 and 20 disjoint classes can be accessed during the training, validation, and testing phases of meta-learning. |
| Hardware Specification | Yes | Our codes are run on a server equipped with an Intel Core i7-12700 CPU, and an NVIDIA RTX A5000 GPU. |
| Software Dependencies | No | The paper mentions optimizers like SGD with Nesterov momentum and Adam but does not list specific software libraries or their version numbers (e.g., PyTorch, Python, CUDA versions) used for implementation. |
| Experiment Setup | Yes | The hyperparameters used for the few-shot classification experiments are the same as those in MAML [10], which are listed in Table 6. To enhance the statbility of the training process, we use SGD with Nesterov momentum instead of Adam as the optimizer for (10a). |