Transductive Zero-Shot Recognition via Shared Model Space Learning
Authors: Yuchen Guo, Guiguang Ding, Xiaoming Jin, Jianmin Wang
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct comprehensive experiments on three benchmark datasets for ZSR. The results demonstrates that the proposed SMS can significantly outperform the state-of-the-art related approaches which validates its efficacy for the ZSR task.We conduct extensive experiments on three benchmark datasets for ZSR. |
| Researcher Affiliation | Academia | Yuchen Guo, Guiguang Ding, Xiaoming Jin and Jianmin Wang School of Software, Tsinghua University, Beijing 100084, China |
| Pseudocode | Yes | Algorithm 1 Transductive ZSR with Shared Model Space |
| Open Source Code | No | The paper does not provide any explicit statement about open-sourcing the code for the described methodology, nor does it include a link to a code repository. |
| Open Datasets | Yes | The first dataset is Animal with Attributes (Aw A) (Lampert, Nickisch, and Harmeling 2014).The second dataset is a Pascal-a Yahoo (a PY) (Farhadi et al. 2009).The third dataset is SUN scene recognition dataset (Patterson and Hays 2012). |
| Dataset Splits | Yes | For Aw A and a PY datasets, we perform 4-fold CV. For SUN dataset, we perform 10-fold CV. Specifically, to perform k-fold CV, we split the source classes equally into k parts. In each fold, we choose one part as the validation set and the other k 1 parts form the training set. |
| Hardware Specification | Yes | The speed is measured using a computer with Intel Core i7-2600 3.40 GHz CPU and 16GB memory. |
| Software Dependencies | No | The paper mentions ‘De CAF’ for feature extraction but does not provide specific version numbers for any software, libraries, or frameworks used in the implementation or experiments. |
| Experiment Setup | Yes | Our approach has two hyper parameters, α and β. In this paper, we adopt the cross validation (CV) to determine the values for them. ... In addition, the values for α and β are selected from {0.01, 0.1, 1, 10, 100}. |