Revealing the Proximate Long-Tail Distribution in Compositional Zero-Shot Learning
Authors: Chenyi Jiang, Haofeng Zhang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that our approach elevates the model s performance to the state-of-the-art level, without introducing additional parameters. |
| Researcher Affiliation | Academia | School of Computer Science and Engineering, Nanjing University of Science and Technology, China {jiangchenyi, zhanghf}@njust.edu.cn |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code or explicitly state its release. |
| Open Datasets | Yes | MIT-States (Isola, Lim, and Adelson 2015), UT-Zappos (Yu and Grauman 2014), and C-GQA (Naeem et al. 2021). |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) for train/validation/test sets. It mentions 'seen' and 'unseen' compositions but not explicit validation splits. |
| Hardware Specification | Yes | The overall model is trained using the Adam optimizer (Kingma and Ba 2014) on NVIDIA GTX 2080Ti GPU |
| Software Dependencies | No | The paper mentions 'Py Torch (Paszke et al. 2019)' but does not provide a specific version number for PyTorch or any other software dependency. |
| Experiment Setup | Yes | We set the learning rate as 5 10 4 and the batchsize as 128. We train the Cs, Co and Cy with an early-stopping strategy, it needs about 400 epochs on MITStates, 300 epochs on UT-Zappos and 400 epochs on CGQA. For hyper-parameters, we set τ as 0.1, 0.1, 0.01, η as 1.0, 1.0, 1.0 and λ as 50, 10, 100 for MIT-States, UTZappos, and C-GQA, respectively. |