Bias and Generalization in Deep Generative Models: An Empirical Study
Authors: Shengjia Zhao, Hongyu Ren, Arianna Yuan, Jiaming Song, Noah Goodman, Stefano Ermon
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper we propose a framework to systematically investigate bias and generalization in deep generative models of images. Using this framework, we are able to systematically evaluate generalization patterns of state-of-the-art models such as GAN [5] and VAE [4]. |
| Researcher Affiliation | Academia | Shengjia Zhao , Hongyu Ren , Arianna Yuan, Jiaming Song, Noah Goodman, Stefano Ermon Stanford University {sjzhao,hyren,xfyuan,tsong,ngoodman,ermon}@stanford.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code available at https://github.com/ermongroup/BiasAndGeneralization |
| Open Datasets | Yes | We use two different datasets for this experiment: a toy dataset where there are k non-overlapping dots (with random color and location) in the image, as in the numerosity estimation task in cognitive psychology [27, 28], and the CLEVR dataset where there are k objects (with random shape, color, location and size) in the scene [29]. Three MNIST: We use images that contain three MNIST digits. |
| Dataset Splits | No | The paper mentions 'training dataset' and 'training set' multiple times and uses large datasets (100k-1M examples), but does not explicitly provide details about training/validation/test splits, percentages, or sample counts. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as GPU or CPU models, or cloud computing specifications. |
| Software Dependencies | No | The paper mentions using WGAN-GP [26] and VAE [4] model families, but does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions, or library versions). |
| Experiment Setup | No | The paper mentions using 'different network architectures and hyper-parameter choices', but it does not provide specific details such as learning rates, batch sizes, number of epochs, or optimizer settings in the main text. |