Meta-AdaM: An Meta-Learned Adaptive Optimizer with Momentum for Few-Shot Learning
Authors: Siyuan Sun, Hongyang Gao
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments to evaluate the proposed Meta-Ada M using three benchmark datasets. Extensive experimental results on benchmark datasets demonstrate the effectiveness of the proposed Meta-Ada M. |
| Researcher Affiliation | Academia | Siyuan Sun Department of Computer Science Iowa State University Ames, IA 50011 sxs14473@iastate.edu Hongyang Gao Department of Computer Science Iowa State University Ames, IA 50011 hygao@iastate.edu |
| Pseudocode | Yes | Algorithm 1 Meta Momentum Inner. Algorithm 2 Meta-Ada M. |
| Open Source Code | No | The paper does not provide a link to open-source code for the described methodology or explicitly state that the code is released. |
| Open Datasets | Yes | We evaluate the proposed methods using three datasets: Mini-Image Net [10], Tiered Image Net [33], and Cifar100 [16] datasets. These datasets are bench-marking datasets in the FSL domain. |
| Dataset Splits | Yes | Following previous works [30], we split 100 classes into 64, 16, and 20 class groups for training, validation, and testing. |
| Hardware Specification | Yes | We use the experimental setting of 5way-1-shot on the Mini-Image Net dataset and record the running time on an NVIDIA RTX A4000 GPU. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python version, specific deep learning frameworks like PyTorch or TensorFlow versions). |
| Experiment Setup | Yes | We show the hyperparamters in Table 2, which applies to each dataset and each backbone. Table 2: Hyperparameters for experiment. Hyperpameter Value tasks batch size 2 inner learning rate η 0.01 outer learning rate α, β 0.001 # inner fine-tune step 5 # training epochs 100 # outer steps in each epoch 500 |