Controlled Training Data Generation with Diffusion Models: Adversarial and Guided Prompts

Core Concepts

Efficiently generate training data using model-informed feedback and target distribution guidance.

Abstract

The content discusses the generation of training data using diffusion models, focusing on adversarial and guided prompts. It outlines a framework for controlled data generation, presents experiments on semantic classification and depth estimation tasks, and compares different methods for generating useful training data. Introduction to Controlled Training Data Generation with Diffusion Models. Framework for generating model- and target distribution-informed training examples. Experiments on semantic classification (Waterbirds dataset) and depth estimation (Taskonomy dataset). Comparison of methods including Agnostic Prompts, Adversarial Prompts, Guided Prompts, and Guided Adversarial Prompts. Results showing the effectiveness of Guided Adversarial Prompts in improving data efficiency. Fine-tuning experiments with different models showcasing the customization of data to specific models.

Stats

"Adversarial Prompts works on par with the Agnostic Prompts in a low-data regime." "Guided Adversarial Prompts combining both feedback mechanisms results in more data-efficient generations outperforming all other methods." "Model feedback generates data tailored to a specific model."

Quotes

"No Extra Data: We train a ResNet50 [26] model using the original training data Dtrain without using any extra data." "Agnostic Prompts: We use a prompt that is not informed of the model or the target distribution." "Guided Adversarial Prompts combines the benefits of both model- and target-informed feedback mechanisms."

Key Insights Distilled From

Controlled Training Data Generation with Diffusion Models

by Teresa Yeo,A... at arxiv.org 03-25-2024

https://arxiv.org/pdf/2403.15309.pdf

Deeper Inquiries

How can controlled training data generation impact deep learning models beyond supervised learning

Controlled training data generation can have a significant impact on deep learning models beyond supervised learning by improving model generalization, enhancing robustness to distribution shifts, and increasing data efficiency. By generating training data that is specifically tailored to the model's failure modes and target distribution, controlled training data generation can help in adapting models to new test distributions, reducing overfitting, and improving performance on challenging tasks. This approach can also lead to better utilization of limited labeled data by creating diverse and relevant examples for model training.

What are potential drawbacks or limitations of relying solely on adversarial prompts for generating training data

Relying solely on adversarial prompts for generating training data may have some drawbacks or limitations. One potential limitation is that adversarial prompts may focus only on fooling the model rather than producing diverse and representative examples from the target distribution. This could result in generated data that is not aligned with the actual test distribution or lacks diversity in terms of different scenarios or classes. Additionally, optimizing solely based on adversarial feedback may lead to overfitting to specific failure modes of the model rather than capturing a more comprehensive understanding of the underlying task.

How might the concept of controlled training data generation be applied in fields outside of machine learning

The concept of controlled training data generation can be applied in various fields outside of machine learning where synthetic or curated datasets are used for training purposes. For example: Healthcare: In medical imaging, controlled generation of synthetic images representing different pathologies can aid in developing robust diagnostic models. Robotics: Generating diverse simulated environments with varying conditions (e.g., lighting, obstacles) can improve robot navigation algorithms. Finance: Creating synthetic financial datasets with different market conditions can help train predictive models for stock price forecasting. Climate Science: Generating climate simulation datasets with controlled variables (temperature, humidity) can assist in building accurate weather prediction models. By customizing training data based on specific requirements and constraints across various domains, practitioners can enhance the performance and adaptability of their AI systems.