Closed-form Sample Probing for Learning Generative Models in Zero-shot Learning
Authors: Samet Cetin, Orhun Buğra Baran, Ramazan Gokberk Cinbis
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 EXPERIMENTS Datasets. We use the four mainstream GZSL benchmark datasets: Caltech-UCSD-Birds (CUB) (Wah et al., 2011), SUN Attribute (SUN) (Patterson & Hays, 2012), Animals with Attributes 2 (AWA2, more simply AWA) (Xian et al., 2018a) and Oxford Flowers (FLO) (Nilsback & Zisserman, 2008). |
| Researcher Affiliation | Academia | Samet Cetin, Orhun Bugra Baran & Ramazan Gokberk Cinbis Middle East Technical University Department of Computer Engineering Ankara, Turkey {cetin.samet,bugra.baran,gcinbis}@metu.edu.tr |
| Pseudocode | No | The paper includes figures illustrating the framework and compute graph but does not contain a formal pseudocode block or algorithm steps. |
| Open Source Code | No | We will provide our source code on a public repository. |
| Open Datasets | Yes | We use the four mainstream GZSL benchmark datasets: Caltech-UCSD-Birds (CUB) (Wah et al., 2011), SUN Attribute (SUN) (Patterson & Hays, 2012), Animals with Attributes 2 (AWA2, more simply AWA) (Xian et al., 2018a) and Oxford Flowers (FLO) (Nilsback & Zisserman, 2008). |
| Dataset Splits | Yes | Therefore, to obtain comparable results within our experiments, we use the following policy to tune the hyper-parameters of our approach and our baselines: we first leave-out 20% of train class samples as val-seen samples. We periodically train a supervised classifier by taking synthetic samples from the generative model, and evaluate it on the validation set, consisting of the aforementioned val-seen samples plus the val-unseen samples with respect to the benchmark splits. |
| Hardware Specification | No | The numerical calculations were partially performed at TUBITAK ULAKBIM, High Performance and Grid Computing Center (TRUBA resources). This mentions a computing center but lacks specific hardware details like GPU/CPU models or memory. |
| Software Dependencies | No | The paper mentions using frameworks like c WGAN, Lis GAN, TF-VAEGAN, FREE, and ESZSL, and a ResNet-101 backbone, but it does not specify software versions for any libraries, dependencies, or programming languages. |
| Experiment Setup | No | The paper describes general experimental settings such as using a ResNet-101 backbone and fine-tuning procedures, and outlines a hyper-parameter tuning policy. However, it does not explicitly provide concrete hyperparameter values (e.g., learning rates, batch sizes, number of epochs) for the experiments conducted in the main text. |