Self-Improving Diffusion Models with Synthetic Data

• URL: http://arxiv.org/abs/2408.16333v1
• Date: Thu, 29 Aug 2024 08:12:18 GMT
• Title: Self-Improving Diffusion Models with Synthetic Data
• Authors: Sina Alemohammad, Ahmed Imtiaz Humayun, Shruti Agarwal, John Collomosse, Richard Baraniuk,
• Abstract summary: 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.
• Score: 12.597035060380001
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The artificial intelligence (AI) world is running out of real data for training increasingly large generative models, resulting in accelerating pressure to train on synthetic data. Unfortunately, training new generative models with synthetic data from current or past generation models creates an autophagous (self-consuming) loop that degrades the quality and/or diversity of the synthetic data in what has been termed model autophagy disorder (MAD) and model collapse. Current thinking around model autophagy recommends that synthetic data is to be avoided for model training lest the system deteriorate into MADness. In this paper, we take a different tack that treats synthetic data differently from real data. Self-IMproving diffusion models with Synthetic data (SIMS) is a new training concept for diffusion models that uses self-synthesized data to provide negative guidance during the generation process to steer a model's generative process away from the non-ideal synthetic data manifold and towards the real data distribution. We demonstrate that SIMS is capable of self-improvement; it establishes new records based on the Fr\'echet inception distance (FID) metric for CIFAR-10 and ImageNet-64 generation and achieves competitive results on FFHQ-64 and ImageNet-512. Moreover, SIMS is, to the best of our knowledge, the first prophylactic generative AI algorithm that can be iteratively trained on self-generated synthetic data without going MAD. As a bonus, SIMS can adjust a diffusion model's synthetic data distribution to match any desired in-domain target distribution to help mitigate biases and ensure fairness.

Related papers

• 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)
• Self-Correcting Self-Consuming Loops for Generative Model Training [16.59453827606427]
  Machine learning models are increasingly trained on a mix of human- and machine-generated data. Despite the successful stories of using synthetic data for representation learning, using synthetic data for generative model training creates "self-consuming loops" Our paper aims to stabilize self-consuming generative model training by introducing an idealized correction function.
  arXiv  Detail & Related papers  (2024-02-11T02:34:42Z)
• Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark [56.8042116967334]
  Synthetic data serves as an alternative in training machine learning models. ensuring that synthetic data mirrors the complex nuances of real-world data is a challenging task. This paper explores the potential of integrating data-centric AI techniques to guide the synthetic data generation process.
  arXiv  Detail & Related papers  (2023-10-25T20:32:02Z)
• Private Synthetic Data Meets Ensemble Learning [15.425653946755025]
  When machine learning models are trained on synthetic data and then deployed on real data, there is often a performance drop. We introduce a new ensemble strategy for training downstream models, with the goal of enhancing their performance when used on real data.
  arXiv  Detail & Related papers  (2023-10-15T04:24:42Z)
• On the Stability of Iterative Retraining of Generative Models on their own Data [56.153542044045224]
  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.
  arXiv  Detail & Related papers  (2023-09-30T16:41:04Z)
• 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)
• Differentially Private Diffusion Models Generate Useful Synthetic Images [53.94025967603649]
  Recent studies have found that, by default, the outputs of some diffusion models do not preserve training data privacy. By privately fine-tuning ImageNet pre-trained diffusion models with more than 80M parameters, we obtain SOTA results on CIFAR-10 and Camelyon17. Our results demonstrate that diffusion models fine-tuned with differential privacy can produce useful and provably private synthetic data.
  arXiv  Detail & Related papers  (2023-02-27T15:02:04Z)
• Contrastive Model Inversion for Data-Free Knowledge Distillation [60.08025054715192]
  We propose Contrastive Model Inversion, where the data diversity is explicitly modeled as an optimizable objective. Our main observation is that, under the constraint of the same amount of data, higher data diversity usually indicates stronger instance discrimination. Experiments on CIFAR-10, CIFAR-100, and Tiny-ImageNet demonstrate that CMI achieves significantly superior performance when the generated data are used for knowledge distillation.
  arXiv  Detail & Related papers  (2021-05-18T15:13:00Z)
• STAN: Synthetic Network Traffic Generation with Generative Neural Models [10.54843182184416]
  This paper presents STAN (Synthetic network Traffic generation with Autoregressive Neural models), a tool to generate realistic synthetic network traffic datasets. Our novel neural architecture captures both temporal dependencies and dependence between attributes at any given time. We evaluate the performance of STAN in terms of the quality of data generated, by training it on both a simulated dataset and a real network traffic data set.
  arXiv  Detail & Related papers  (2020-09-27T04:20:02Z)

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.