Evolutionary Generalized Zero-Shot Learning
Authors: Dubing Chen, Chenyi Jiang, Haofeng Zhang
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on three popular GZSL benchmark datasets demonstrate that our model can learn from the test data stream while other baselines fail. |
| Researcher Affiliation | Academia | School of Artificial Intelligence, Nanjing University of Science and Technology |
| Pseudocode | Yes | Algorithm 1 The Proposed EGZSL Method |
| Open Source Code | Yes | The codes are available at https://github.com/cdb342/EGZSL. |
| Open Datasets | Yes | We evaluate EGZSL methods on three public ZSL benchmarks: 1) Animals with Attributes 2 (AWA2) [Lampert et al., 2013] contains 50 animal species and 85 attribute annotations, accounting for 37,322 samples. 2) Attribute Pascal and Yahoo (APY) [Farhadi et al., 2009] includes 32 classes of 15,339 samples and 64 attributes. 3) Caltech-UCSD Birds200-2011 (CUB) [Wah et al., 2011] consists of 11,788 samples of 200 bird species, annotated by 312 attributes. |
| Dataset Splits | Yes | for a given ZSL dataset, the original training set serves as the base set, while the test set is partitioned into various batches in a fixed random order. We split the data into seen and unseen classes according to the common GZSL benchmark procedure in [Xian et al., 2017]. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Py Torch function' and 'Adam optimizer' but does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | We employ the Adam optimizer [Kingma and Ba, 2015] with a learning rate of 5e-5 for the main experiments. We set the (mini) batch size equal to the total number of data in each evolutionary stage. Each stage of data is optimized for one epoch only. We set λ at 1, τ at 0.5, m1 at 0.99, and m2 at 0.9 for the best results. |