Accumulating Real and Synthetic Data Prevents Model Collapse in Generative Models

Core Concepts

Accumulating real and synthetic data prevents the performance degradation known as model collapse, which occurs when generative models are trained on their own generated outputs.

Abstract

The paper investigates the phenomenon of model collapse, where the performance of generative models progressively degrades when trained on their own generated outputs. The authors begin by studying an analytically tractable setup involving a sequence of linear models, and prove that if data accumulates across iterations, the test error has a finite upper bound independent of the number of iterations. This contrasts with prior work that assumed data replacement, where the test error grows linearly with the number of iterations.
The authors then empirically test their conjecture on deep generative models trained on real data:

For causal transformer language models trained on text data (TinyStories), replacing data leads to increasing test cross-entropy, while accumulating data prevents this.
For diffusion models trained on molecular data (GEOM-Drugs), replacing data causes test loss to degrade, while accumulating data maintains stable performance.
For variational autoencoders trained on image data (CelebA), replacing data leads to mode collapse and quality degradation, while accumulating data preserves sample quality and diversity.
The results provide consistent theoretical and empirical evidence that accumulating real and synthetic data can be a robust solution to mitigate model collapse in generative models.

Stats

The test error grows linearly with the number of model-fitting iterations when data are replaced, but has a finite upper bound when data accumulate.
For language models, test cross-entropy increases when replacing data, but remains stable when accumulating data.
For diffusion models, test loss degrades when replacing data, but remains stable when accumulating data.
For variational autoencoders, replacing data leads to mode collapse and quality degradation, while accumulating data preserves sample quality and diversity.

Quotes

"Accumulating real and synthetic data prevents the performance degradation known as model collapse, which occurs when generative models are trained on their own generated outputs."
"Our work provides consistent theoretical and empirical evidence that data accumulation mitigates model collapse."

Key Insights Distilled From

Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data

by Matthias Ger... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01413.pdf

Deeper Inquiries

How do the dynamics of model collapse differ across various generative model architectures and data modalities

The dynamics of model collapse can vary across different generative model architectures and data modalities. In the context provided, the experiments conducted on causal transformers for language modeling, diffusion models for molecule generation, and variational autoencoders for image data showed that accumulating data prevented model collapse. Specifically, when data was accumulated across model-fitting iterations, the test error either plateaued or increased at a much slower rate compared to when data was replaced at each iteration. This indicates that accumulating data can be a robust solution to mitigate model collapse in various generative models.

What are potential drawbacks or limitations of accumulating data to prevent model collapse, and under what conditions might this approach fail

While accumulating data can be effective in preventing model collapse in certain scenarios, there are potential drawbacks and limitations to consider. One limitation is the increased computational complexity and resource requirements associated with accumulating data over multiple iterations. As the dataset grows with each iteration, training models on larger datasets can be more computationally intensive and time-consuming. Additionally, accumulating data may not completely eliminate the risk of model collapse in all situations. For instance, if the synthetic data generated by the model introduces biases or errors that are perpetuated and amplified over time, accumulating data may not be sufficient to prevent model degradation. Furthermore, the effectiveness of accumulating data may depend on the specific characteristics of the generative model, the dataset, and the training process, and there may be cases where accumulating data does not provide a significant improvement in preventing model collapse.

How can the insights from this work on model collapse be extended to other areas of machine learning, such as self-supervised learning or reinforcement learning, where models may also be trained on their own outputs

The insights gained from studying model collapse in generative models can be extended to other areas of machine learning, such as self-supervised learning and reinforcement learning, where models may also be trained on their own outputs. In self-supervised learning, where models learn representations from unlabeled data, the concept of model collapse can manifest as the degradation of representation quality over training iterations. By applying the principle of accumulating data and ensuring a diverse and representative dataset for training, self-supervised models can potentially avoid representation collapse and maintain high-quality features. Similarly, in reinforcement learning, where agents learn from interactions with the environment, accumulating diverse experiences and preventing the agent from getting stuck in a suboptimal policy can be crucial to avoid policy collapse. By incorporating strategies to accumulate diverse experiences and prevent overfitting to the agent's own generated data, reinforcement learning models can improve their learning stability and performance over time.