FairQueue: Rethinking Prompt Learning for Fair Text-to-Image Generation
Authors: Christopher Teo, Milad Abdollahzadeh, Xinda Ma, Ngai-Man (Man) Cheung
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on a wide range of t SAs show that our proposed method outperforms SOTA approach s image generation quality, while achieving competitive fairness. In this section, we evaluate our proposed (Fair Queue) against the existing SOTA ITI-GEN [16] over various t SA. Then, we conduct an ablation study by first evaluating the contribution brought by each component for Fair Queue i.e., Prompt queuing, and Attention Scaling. |
| Researcher Affiliation | Academia | Christopher T. H. Teo christopher_teo@mymail.sutd.edu.sg Milad Abdollahzadeh milad_abdollahzadeh@sutd.sg Xinda Ma xinda_ma@sutd.edu.sg Ngai-Man Cheung ngaiman_cheung@sutd.edu.sg Singapore University of Technology and Design (SUTD) |
| Pseudocode | No | The paper describes its methods through textual explanations and mathematical equations, but it does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | In addition, to facilitate reproducibility we have also provided the anonymous link for the code used in this paper. |
| Open Datasets | Yes | Following [16], we utilize the publicly available reference dataset from Celeb A [29], Fair Face [45] and FAIR benchmark [44]. In addition, all datasets used in this paper are publicly available. |
| Dataset Splits | No | The paper describes the datasets used and mentions repeating experiments for statistical significance ('We repeat this process 5 times'), but it does not specify explicit training, validation, or test dataset splits (e.g., percentages or sample counts) for its own experimental setup beyond referencing existing datasets. |
| Hardware Specification | Yes | T2I Sample Generation RTX3090 25.0 4.87 |
| Software Dependencies | No | The paper mentions using 'Stable Diffusion v1.4 [1]' and an 'Adam [48] optimizer' but does not provide specific version numbers for other key ancillary software components like Python, PyTorch/TensorFlow, or CUDA, which are necessary for full reproducibility. |
| Experiment Setup | Yes | For sample generation, we follow the recommended diffusion steps of l = 50 and utilize an Attention scale of c = 10 and an Attention Queuing transitioning step =10. Si with a token length of 3 per t SA which is optimized based on a learning rate of lr = 0.01. |