On the Stability of Iterative Retraining of Generative Models on their own Data

• URL: http://arxiv.org/abs/2310.00429v5
• Date: Tue, 2 Apr 2024 14:09:40 GMT
• Title: On the Stability of Iterative Retraining of Generative Models on their own Data
• Authors: Quentin Bertrand, Avishek Joey Bose, Alexandre Duplessis, Marco Jiralerspong, Gauthier Gidel,
• Abstract summary: We study the impact of training generative models on mixed datasets. We first prove the stability of iterative training under the condition that the initial generative models approximate the data distribution well enough. We empirically validate our theory on both synthetic and natural images by iteratively training normalizing flows and state-of-the-art diffusion models.
• Score: 56.153542044045224
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep generative models have made tremendous progress in modeling complex data, often exhibiting generation quality that surpasses a typical human's ability to discern the authenticity of samples. Undeniably, a key driver of this success is enabled by the massive amounts of web-scale data consumed by these models. Due to these models' striking performance and ease of availability, the web will inevitably be increasingly populated with synthetic content. Such a fact directly implies that future iterations of generative models will be trained on both clean and artificially generated data from past models. In this paper, we develop a framework to rigorously study the impact of training generative models on mixed datasets -- from classical training on real data to self-consuming generative models trained on purely synthetic data. We first prove the stability of iterative training under the condition that the initial generative models approximate the data distribution well enough and the proportion of clean training data (w.r.t. synthetic data) is large enough. We empirically validate our theory on both synthetic and natural images by iteratively training normalizing flows and state-of-the-art diffusion models on CIFAR10 and FFHQ.

Related papers

• Will the Inclusion of Generated Data Amplify Bias Across Generations in Future Image Classification Models? [29.71939692883025]
  We investigate the effects of generated data on image classification tasks, with a specific focus on bias. Hundreds of experiments are conducted on Colorized MNIST, CIFAR-20/100, and Hard ImageNet datasets. Our findings contribute to the ongoing debate on the implications of synthetic data for fairness in real-world applications.
  arXiv  Detail & Related papers  (2024-10-14T05:07:06Z)
• Self-Improving Diffusion Models with Synthetic Data [12.597035060380001]
  Self-IM diffusion models with Synthetic data (SIMS) is a new training concept for diffusion models. SIMS uses self-synthesized data to provide negative guidance during the generation process. It is the first prophylactic generative AI algorithm that can be iteratively trained on self-generated synthetic data without going MAD.
  arXiv  Detail & Related papers  (2024-08-29T08:12:18Z)
• Self-Consuming Generative Models with Curated Data Provably Optimize Human Preferences [20.629333587044012]
  We study the impact of data curation on iterated retraining of generative models. We prove that, if the data is curated according to a reward model, the expected reward of the iterative retraining procedure is maximized.
  arXiv  Detail & Related papers  (2024-06-12T21:28:28Z)
• Heat Death of Generative Models in Closed-Loop Learning [63.83608300361159]
  We study the learning dynamics of generative models that are fed back their own produced content in addition to their original training dataset. We show that, unless a sufficient amount of external data is introduced at each iteration, any non-trivial temperature leads the model to degenerate.
  arXiv  Detail & Related papers  (2024-04-02T21:51:39Z)
• Towards Theoretical Understandings of Self-Consuming Generative Models [56.84592466204185]
  This paper tackles the emerging challenge of training generative models within a self-consuming loop. We construct a theoretical framework to rigorously evaluate how this training procedure impacts the data distributions learned by future models. We present results for kernel density estimation, delivering nuanced insights such as the impact of mixed data training on error propagation.
  arXiv  Detail & Related papers  (2024-02-19T02:08:09Z)
• Learning Defect Prediction from Unrealistic Data [57.53586547895278]
  Pretrained models of code have become popular choices for code understanding and generation tasks. Such models tend to be large and require commensurate volumes of training data. It has become popular to train models with far larger but less realistic datasets, such as functions with artificially injected bugs. Models trained on such data tend to only perform well on similar data, while underperforming on real world programs.
  arXiv  Detail & Related papers  (2023-11-02T01:51:43Z)
• The Big Data Myth: Using Diffusion Models for Dataset Generation to Train Deep Detection Models [0.15469452301122172]
  This study presents a framework for the generation of synthetic datasets by fine-tuning stable diffusion models. The results of this study reveal that the object detection models trained on synthetic data perform similarly to the baseline model.
  arXiv  Detail & Related papers  (2023-06-16T10:48:52Z)
• Synthetic data, real errors: how (not) to publish and use synthetic data [86.65594304109567]
  We show how the generative process affects the downstream ML task. We introduce Deep Generative Ensemble (DGE) to approximate the posterior distribution over the generative process model parameters.
  arXiv  Detail & Related papers  (2023-05-16T07:30:29Z)
• Is synthetic data from generative models ready for image recognition? [69.42645602062024]
  We study whether and how synthetic images generated from state-of-the-art text-to-image generation models can be used for image recognition tasks. We showcase the powerfulness and shortcomings of synthetic data from existing generative models, and propose strategies for better applying synthetic data for recognition tasks.
  arXiv  Detail & Related papers  (2022-10-14T06:54:24Z)
• Forecasting Industrial Aging Processes with Machine Learning Methods [0.0]
  We evaluate a wider range of data-driven models, comparing some traditional stateless models to more complex recurrent neural networks. Our results show that recurrent models produce near perfect predictions when trained on larger datasets.
  arXiv  Detail & Related papers  (2020-02-05T13:06:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.