Task Aligned Generative Meta-learning for Zero-shot Learning
Authors: Zhe Liu, Yun Li, Lina Yao, Xianzhi Wang, Guodong Long8723-8731
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show TGMZ achieves a relative improvement of 2.1%, 3.0%, 2.5%, and 7.6% over state-of-the-art algorithms on AWA1, AWA2, CUB, and a PY datasets, respectively. Overall, TGMZ outperforms competitors by 3.6% in the generalized zero-shot learning (GZSL) setting and 7.9% in our proposed fusion ZSL setting. We conduct extensive experiments on four benchmark datasets: AWA1 (Lampert, Nickisch, and Harmeling 2009), AWA2 (Xian et al. 2019), CUB (Welinder et al. 2010), and a PY (Farhadi et al. 2009). |
| Researcher Affiliation | Academia | Zhe Liu,1* Yun Li, 1 Lina Yao, 1 Xianzhi Wang, 2 Guodong Long 2 1 University of New South Wales, Australia 2 University of Technology Sydney, Australia {zhe.liu1, yun.li5, lina.yao}@unsw.edu.au, {xianzhi.wang, guodong.long}@uts.edu.au |
| Pseudocode | Yes | Algorithm 1 TGMZ Training Procedure |
| Open Source Code | No | The paper does not provide any concrete access to source code (no specific repository link, explicit code release statement, or mention of code in supplementary materials). |
| Open Datasets | Yes | We conduct extensive experiments on four benchmark datasets: AWA1 (Lampert, Nickisch, and Harmeling 2009), AWA2 (Xian et al. 2019), CUB (Welinder et al. 2010), and a PY (Farhadi et al. 2009). |
| Dataset Splits | Yes | Different from conventional ZSL settings, we divide the training set Dtr into a support set Dsup and a disjoint query set Dqry to mimic seen and unseen classes, respectively, during training. We divide the datasets into seen and unseen classes following the proposed split (PS) (Xian et al. 2019) and adopt visual features from pre-trained Res Net-101, according to Xian et al. (Xian et al. 2019). The dataset statistics and train/test split are shown in Table 1. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'pre-trained Res Net-101' and evaluating with 'Linear-SVM' and 'Softmax' but does not specify any software names with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | By default, we set σ = 1 and the sample number to 100. More details about Dataset Description, Model Architecture, Parameter Setting and Convergence Analysis can be found in Supplementary Material. |