Co-Representation Network for Generalized Zero-Shot Learning
Authors: Fei Zhang, Guangming Shi
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach on five benchmark datasets including Aw A1 (Animals with Attributes 1), Aw A2 (Animals with Attributes 2), CUB (Caltech UCSD Birds 200), SUN (SUN Scene Recognition) and a PY(Attribute Pascal and Yahoo), following the GZSL settings (Xian et al., 2017) for seen/unseen splits and compare it with other methods including classic CZSL methods and several recent GZSL methods. |
| Researcher Affiliation | Academia | 1School of Artificial Intelligence, Xidian University, China. Correspondence to: Guangming Shi <gmshi@xidian.edu.cn>. |
| Pseudocode | No | The paper describes the algorithm steps in paragraph text but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it state that the code is available. |
| Open Datasets | Yes | We evaluate our approach on five benchmark datasets including Aw A1 (Animals with Attributes 1), Aw A2 (Animals with Attributes 2), CUB (Caltech UCSD Birds 200), SUN (SUN Scene Recognition) and a PY(Attribute Pascal and Yahoo), following the GZSL settings (Xian et al., 2017) for seen/unseen splits and compare it with other methods including classic CZSL methods and several recent GZSL methods. |
| Dataset Splits | Yes | For AWA1, CUB and SUN, the hyper-parameters are determined through a train-validation split of seen classes and are used to train the model on complete data. For Aw A2 and a PY, we use the same hyper-parameters as Aw A1 because of the similarity of the three datasets. The adjustment of hyper-parameters in our method is not complicated and they follow some rules: The best value of K is roughly positively correlated with the number of seen classes M. And our experiments show that a slight increase in K will bring some redundant parameters to the network, but has little impact on the results. |
| Hardware Specification | No | The paper mentions using features extracted from ResNet-101 but does not provide specific hardware details (like GPU/CPU models, memory, or cloud instances) used for training or running the CRnet experiments. |
| Software Dependencies | No | The paper mentions 'Adam optimizer' and 'ResNet-101' features but does not provide specific version numbers for any software, libraries, or programming languages used. |
| Experiment Setup | Yes | The specific details and training hyper-parameters of each datasets are summarized in Table 2. ... All models are trained at a learning rate of 10 5 with Adam optimizer until the loss converge. |