Self-Consuming Generative Models with Adversarially Curated Data
Authors: Xiukun Wei, Xueru Zhang
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both synthetic and real-world datasets demonstrate the effectiveness of the proposed algorithms. |
| Researcher Affiliation | Academia | Xiukun Wei 1 Xueru Zhang 1 1Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio, USA. Correspondence to: Xueru Zhang <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Gradient-based attack |
| Open Source Code | No | The paper does not contain any explicit statement about providing source code, a link to a code repository, or information about code in supplementary materials for the methodology described. |
| Open Datasets | Yes | Datasets. Similar to Ferbach et al. (2024), we conduct experiments on three datasets: 1. Synthetic Gaussian: A dataset following 8-mode Gaussian mixture model, the details are in Appendix C.2. 2. CIFAR-10 (Krizhevsky, 2009): It contains 60,000 images from 10 classes... 3. CIFAR-100 (Krizhevsky, 2009): It contains 100 classes, each with 600 images. |
| Dataset Splits | Yes | At each iteration, the generative model produces 50,000 data samples, of which 25,000 are selected (after curation by the reward model) for retraining the next-generation model. |
| Hardware Specification | No | The paper mentions conducting experiments but does not provide specific details about the hardware used, such as GPU models, CPU types, or memory. |
| Software Dependencies | No | The paper mentions using specific models like DDPM, VGG11, and ResNet56, but does not provide specific version numbers for any software dependencies or libraries used for implementation and training. |
| Experiment Setup | Yes | At each iteration, the generative model produces 50,000 data samples, of which 25,000 are selected (after curation by the reward model) for retraining the next-generation model. For the reward R and R may share the same architecture or differ. If they have the same architecture, we use pretrained VGG11 as the feature extractor and a linear layer containing 10 neurons for both R and R. If different architectures are used, R employs a pretrained VGG11 as the feature extractor, followed by three linear layers with 128, 64, and 10 neurons, respectively. |