Meta-Learning Priors Using Unrolled Proximal Networks
Authors: Yilang Zhang, Georgios B. Giannakis
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical tests conducted on few-shot learning datasets demonstrate markedly improved performance with flexible, visualizable, and understandable priors. and In this section, numerical tests are presented on several meta-learning benchmark datasets to evaluate the empirical performance of Meta Prox Net. |
| Researcher Affiliation | Academia | Yilang Zhang, Georgios B. Giannakis Department of Electric and Computer Engineering University of Minnesota Minneapolis, MN 55414, USA {zhan7453,georgios}@umn.edu |
| Pseudocode | Yes | Algorithm 1: Vanilla PGD algorithm for solving (1b) and Algorithm 2: Meta Prox Net algorithm |
| Open Source Code | Yes | All experiments are run on a server with RTX A5000 GPU, and our codes are available online at https://github.com/zhangyilang/Meta Prox Net. |
| Open Datasets | Yes | The mini Image Net dataset (Vinyals et al., 2016) consists of 60, 000 natural images sampled from the full Image Net (ILSVRC-12) dataset. and The Tiered Image Net (Ren et al., 2018) dataset is a larger subset of the Image Net dataset, composed of 779, 165 images from 608 classes. |
| Dataset Splits | Yes | The mini Image Net dataset... is split into 3 disjoint groups containing 64, 16 and 20 classes, which can be respectively accessed during the training, validation, and testing phases of meta-learning. |
| Hardware Specification | Yes | All experiments are run on a server with RTX A5000 GPU, and our codes are available online at https://github.com/zhangyilang/Meta Prox Net. |
| Software Dependencies | No | Adam optimizer is employed for tiered Image Net, while SGD with Nesterov momentum of 0.9 and weight decay of 10 4 is used for mini Image Net. The paper does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | The maximum number K of PGD steps (7) is 5, and the total number R of mini-batch SGD iterations (8) is 60, 000. The number of convolutional channels is 64 for Meta Prox Net+MAML, and 128 for Meta Prox Net+MC. The learning rates for PGD and SGD are α = 0.01 and β = 0.001, with batch size B = 4. Adam optimizer is employed for tiered Image Net, while SGD with Nesterov momentum of 0.9 and weight decay of 10 4 is used for mini Image Net. The interval [ A, A] and number C of pieces are determined through a grid search leveraging the validation tasks. For both mini Image Net and Tiered Imge Net datasets, A = 0.02 and C = 5. |