Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Hyperbolic Knowledge Transfer with Class Hierarchy for Few-Shot Learning
Authors: Baoquan Zhang, Hao Jiang, Shanshan Feng, Xutao Li, Yunming Ye, Rui Ye
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three datasets show our method achieves superior performance over state-of-the-art methods, especially on 1-shot tasks. |
| Researcher Affiliation | Academia | Harbin Institute of Technology, Shenzhen |
| Pseudocode | No | The paper describes the steps of the method in prose but does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not include any statement about releasing source code or provide a link to a code repository. |
| Open Datasets | Yes | mini Imagenet. This is a subset of the Image Net, which contains 100 classes and 600 images per class. ... tiered Imagenet. This dataset is also derived from the Image Net dataset. ... CIFAR-FS. This dataset is constructed from CIFAR100 |
| Dataset Splits | Yes | Following the setting of [Peng et al., 2019], we split the data set into 64 classes for training, 16 classes for validation, and 20 classes for test, respectively. |
| Hardware Specification | No | The paper mentions using 'Res Net12 as our backbone' but does not specify any hardware details such as GPU models, CPU types, or memory. |
| Software Dependencies | No | The paper mentions 'Riemannian Adam optimizer' and 'Res Net12' but does not specify any software versions for libraries, frameworks, or programming languages. |
| Experiment Setup | Yes | These hyper-parameters, γ = 1/640, α = 2, β = 1 are used in our all experiments. For hyper-parameters λc and λr, λc = 1 and λr = 2 are used for mini Imagenet, λc = 2 and λr = 4 are used for tiered Imagenet, and λc = 1 and λr = 2 are used for CIFAR-FS. ... we set the initial learning rate to 0.00001 and then decay it by 0.1 at epochs 50, 80 and 90, respectively. ... with a weight decay of 0.001. |