Hierarchically Structured Meta-learning
Authors: Huaxiu Yao, Ying Wei, Junzhou Huang, Zhenhui Li
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results show that our approach can achieve state-of-the-art performance in both toy-regression and few-shot image classification problems. |
| Researcher Affiliation | Collaboration | 1College of Information Science and Technology, Pennsylvania State University, PA, USA 2Tencent AI Lab, Shenzhen, China. |
| Pseudocode | Yes | Algorithm 1 Meta-training of HSML |
| Open Source Code | No | The paper does not provide any explicit statements about open-sourcing code or links to a code repository. |
| Open Datasets | Yes | Caltech-UCSD Birds-200-2011 (Bird) (Wah et al., 2011), Describable Textures Dataset (Texture) (Cimpoi et al., 2014), Fine-Grained Visual Classification of Aircraft (Aircraft) (Maji et al., 2013), and FGVCx-Fungi (Fungi) (Fun, 2018) |
| Dataset Splits | Yes | Similar to the preprocessing of Mini Imagenet (Vinyals et al., 2016), we divide each dataset to meta-training, meta-validation and meta-testing classes. |
| Hardware Specification | No | The paper does not provide specific hardware details such as CPU/GPU models or cloud configurations used for experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper states 'We specify the hyperparameters for meta-training in supplementary material C.' but does not include these specific details in the main text. |