Deconstructed Generation-Based Zero-Shot Model
Authors: Dubing Chen, Yuming Shen, Haofeng Zhang, Philip H.S. Torr
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present a simple method based on this analysis that outperforms Sot As on four public GZSL datasets, demonstrating the validity of our deconstruction. |
| Researcher Affiliation | Academia | Dubing Chen1, Yuming Shen2, Haofeng Zhang1*, Philip H.S. Torr2 1 Nanjing University of Science and Technology 2 University of Oxford |
| Pseudocode | No | The paper describes methods in prose and equations, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/cdb342/DGZ. |
| Open Datasets | Yes | We conduct GZSL experiments on four public ZSL datasets. Animals with Attributes 2 (AWA2) (Lampert, Nickisch, and Harmeling 2013) ... Attribute Pascal and Yahoo (APY) (Farhadi et al. 2009) ... Caltech-UCSD Birds-200-2011 (CUB) (Wah et al. 2011) ... SUN Attribute (SUN) (Patterson and Hays 2012) |
| Dataset Splits | Yes | We split the data into seen and unseen classes according to the common benchmark procedure in Xian, Schiele, and Akata (2017). |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for experiments (e.g., GPU models, CPU types, or cloud computing specifications). |
| Software Dependencies | No | The paper mentions using WGAN-GP and Adam optimizer, but does not specify software versions for libraries, frameworks, or programming languages (e.g., PyTorch version, Python version). |
| Experiment Setup | Yes | The Generator G carries two hidden layers with 4096 and 2048 dimensions. The Discriminator D contains one 4096-D hidden layer, and the mapping net M includes a 1024-D hidden layer. All the hidden layers are activated by Leaky-Re LU. ... we put 512 for the (mini) batch size and adopt Adam (Kingma and Ba 2015) as the optimizer with a learning rate of 1.0 10 4. ... We set τ to 0.04... We empirically generate 50 samples per unseen class in CUB, SUN, and APY, and 100 for AWA2 in all experiments. We put λ1 to 4, 0.8, 0.04, and 0.005 for the above datasets. σ is set to 0.08 on all datasets. |