Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Self-Consuming Generative Models with Adversarially Curated Data
Authors: Xiukun Wei, Xueru Zhang
ICML 2025 | Venue PDF | LLM Run Details | Input Tokens: 24,444 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,264 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |