Self-Consuming Generative Models Go MAD (AI summary)

Key Points

1. Without enough fresh real data in each generation of an autophagous loop, future generative models are likely to experience a degradation in their quality or diversity, leading to Model Autophagy Disorder (MAD).

2. Synthetic data sourced from generative models has been increasingly integrated into training datasets, with implications for the quality and diversity of the data, particularly in the context of datasets used for training generative models.

3. The paper presents empirical analysis using various generative models and datasets, demonstrating the progressive amplification of biases and artifacts in generative models due to autophagous training. The findings suggest potential consequences such as decreased diversity and amplified artifacts in successive generations of generative models.

4. The authors also discuss the potential implications of autophagous loops on the performance of generative models, emphasizing the need for thorough consideration and study of the unintended consequences that could arise from such loops.

Summary

The research paper investigates the phenomenon of "Model Autophagy Disorder (MAD)" in the context of generative models used in artificial intelligence. The paper highlights the potential consequences of repeatedly training generative models on synthetic data, leading to a progressive degradation of the quality and diversity of the generated outputs. The study focuses on three autophagous loops: the fully synthetic loop, the synthetic augmentation loop, and the fresh data loop, and explores the impact of training generative models in these loops.

The experiments include analyzing the effects of sampling bias, the incorporation of fixed real training data, and the inclusion of fresh real data in successive generations of generative models. The results demonstrate that without enough fresh real data in each generation, future generative models are likely to experience a decrease in quality or diversity, leading to MAD. The study also indicates that the use of synthetic data in generative models may lead to the amplification of artifacts and a decrease in data diversity, especially in scenarios where the bias in sampling synthetic data is employed to increase sample quality at the expense of diversity.

Overall, the paper emphasizes the importance of understanding and addressing the potential detrimental effects of autophagous loops on the properties and performance of generative models, particularly in the context of rapidly advancing generative models and the growing use of synthetic data in training. It also suggests potential avenues for mitigating the negative effects of autophagous loops, such as incorporating enough fresh real data to prevent MAD and exploring watermarking methods to identify and control synthetic data. The paper also encourages further research into understanding the implications of autophagous loops on various types of data and the development of methods to prevent and mitigate MAD in generative models.

Reference: https://arxiv.org/abs/2307.01850