Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Zero-shot Learning via Simultaneous Generating and Learning
Authors: Hyeonwoo Yu, Beomhee Lee
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experimental results, we demonstrate that the proposed generating and learning strategy makes the model achieve the outperforming results compared to that trained only on the seen classes, and also to the several state-of-the-art methods. |
| Researcher Affiliation | Academia | Hyeonwoo Yu Beomhee Lee Automation and Systems Research Institute (ASRI) Dept. of Electrical and Computer Engineering Seoul National University EMAIL |
| Pseudocode | Yes | Algorithm 1 Simultaneously Generating-And-Learning Algorithm |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating the availability of open-source code for the described methodology. |
| Open Datasets | Yes | We ο¬rstly use the two benchmark datasets: Aw A (Animals with Attributes) [16], which contains 30,745 images of 40/10(train/test) classes, and CUB (Caltech-UCSD Birds-200-2011) [26], comprised of 11,788 images of 150/50(train/test) species. |
| Dataset Splits | Yes | SUN is a scene-image dataset and consists of 14,340 images with 645/72/65(train/test/validation) classes. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU/GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions software like Inception-V2, ResNet101, and Adam, but does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | The number of iterations for the benchmarks are: for mm VAE and SGAL(EM), 170,000 and 1,300 for Aw A1, 64,000 and 900 for Aw A2, 17,000 and 2,000 for CUB1 and 1,450,000 and 1,500 for SUN1. In order to consider the model uncertainty, we also train the model adopting (9) when generating unseen datapoints. For one latent variables sampled from prior network, a total of 5 samples are generated while activating dropouts in the decoder. Unlike (9), in the actual implementation all the dropouts of the prior network are deactivated for the training stabilization. |