Debiasing Pretrained Generative Models by Uniformly Sampling Semantic Attributes
Authors: Walter Gerych, Kevin Hickey, Luke Buquicchio, Kavin Chandrasekaran, Abdulaziz Alajaji, Elke A. Rundensteiner, Emmanuel Agu
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on debiasing generators trained on popular real-world datasets show that our method outperforms state-of-the-art approaches. |
| Researcher Affiliation | Collaboration | Walter Gerych1 , Kevin Hickey1, Luke Buquicchio1, Kavin Chandrasekaran1, Abdulaziz Alajaji2, Elke Rundensteiner1, Emmanuel Agu1 1Worcester Polytechnic Institute, Worcester, MA 2King Saud University, Riyadh, Saudi Arabia |
| Pseudocode | Yes | Algorithm 1 Naively collect data from Q ... Algorithm 2 Collecting a dataset of samples from Qλ |
| Open Source Code | Yes | Code for our method is available in the supplemental material. |
| Open Datasets | Yes | Specifically, the generative model is a DCGAN [32] that we pretrain on a grayscale version of the UTKFace dataset [44]. ... We use the Race of the individual in each image as the semantic attribute, and use the MTCNN classifier from the Deep Face [36] package to classify race. ... Although our focus is primrily on models that map from low dimensional latent space to a higher dimensional feature space, we also evaluate our approach on a latent diffusion model 3 [35] that was trained on the Celeba-HQ dataset [19]. |
| Dataset Splits | No | The paper does not explicitly provide specific training/validation/test dataset splits with percentages, sample counts, or references to predefined splits for reproduction. |
| Hardware Specification | No | The paper mentions 'a high-performance computing system acquired through NSF MRI grant DMS-1337943 to WPI' in the acknowledgments but does not provide specific hardware details such as GPU/CPU models or processor types. |
| Software Dependencies | Yes | The distribution mapper used default architecture of SDV s CTGAN 5 version 0.6.0, except for in the Progressive GAN experiment where embedding_dim =512, generator_dim =(512,512) were passed as arguments. |
| Experiment Setup | Yes | Details such as hyperparameter choice and architectures are available in the appendix. ... For the networks we trained, we utilized the Adam optimizer with learning rate between 0.002 and 0.0001. |