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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Contrastive Fine-grained Class Clustering via Generative Adversarial Networks
Authors: Yunji Kim, Jung-Woo Ha
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 EXPERIMENTS |
| Researcher Affiliation | Industry | Yunji Kim NAVER AI Lab EMAIL; Jung-Woo Ha NAVER AI Lab & NAVER CLOVA EMAIL |
| Pseudocode | No | The paper describes the model architecture and objective functions using equations and tables, but it does not include a block explicitly labeled as 'Pseudocode' or 'Algorithm'. |
| Open Source Code | Yes | Code is available at https://github.com/naver-ai/c3-gan. |
| Open Datasets | Yes | We tested our method on 4 datasets that consist of single object images. i) CUB (Wah et al., 2011): 5,994 training and 5,794 test images of 200 bird species. ii) Stanford Cars (Krause et al., 2013): 8,144 training and 8,041 test images of 196 car models. iii) Stanford Dogs (Khosla et al., 2011): 12,000 training and 8,580 test images of 120 dog species. iv) Oxford Flower (Nilsback & Zisserman, 2008): 2,040 training and 6,149 test images of 102 flower categories. |
| Dataset Splits | No | For example, for CUB: '5,994 training and 5,794 test images'. The paper lists training and test image counts for each dataset but does not explicitly mention a separate validation split or how it's handled for training. |
| Hardware Specification | Yes | The training was done with 2 NVIDIA-V100 GPUs |
| Software Dependencies | No | The paper mentions 'Adam optimizer' and 'Inception networks' but does not specify any software names with version numbers for libraries or programming languages used. |
| Experiment Setup | Yes | The weights of the loss terms (λ0, λ1, λ2, λ3, λ4) are set as (5, 1, 1, 0.1, 1), and the temperature τ is set as 0.1. We utilized Adam optimizer of which learning rate is 0.0002 and values of momentum coefficients are (0.5, 0.999). |