Diffusion Model Training With Limited Text Labels
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Solution Overview
Problem
Training generative AI models with limited text labels leads to unsatisfactory performance due to issues like overfitting, bias, and lack of diversity, especially in domains requiring high expertise for annotation.
Innovation Solution
A two-stage diffusion-based framework is employed to train a diffusion neural network model. The first stage uses unsupervised training on an unlabeled subset of the dataset, followed by fine-tuning with a labeled subset, ensuring that the model learns a conditional data distribution conditioned on text labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If training data is annotated with text labels to improve model performance, then model accuracy and controllability improve, but annotation cost and time increase significantly
Solution Approach 1:
The system performs preliminary unsupervised learning on unlabeled data before supervised fine-tuning on labeled data. This preliminary action allows the model to learn general data distributions and patterns from abundant unlabeled data, reducing the need for extensive manual annotation while still achieving good performance.
Solution Approach 2:
The training process is segmented into two distinct stages: unsupervised pre-training on unlabeled data and supervised fine-tuning on labeled data. This segmentation allows the system to efficiently utilize both unlabeled and labeled data, reducing the proportion of labeled data needed while maintaining model performance.
2Reliability
If more labeled training data is used to reduce overfitting and improve diversity, then model generalization improves, but annotation cost increases
Solution Approach 1:
The system uses a small proportion of labeled data (as little as 10% or less) for supervised fine-tuning after unsupervised pre-training. This partial action approach achieves good generalization performance without requiring extensive labeled data, making data preparation much easier and more cost-effective.
Solution Approach 2:
The unsupervised pre-training stage allows the model to self-learning from unlabeled data without human annotation. This self-service capability enables the model to learn general patterns and reduce overfitting on its own, reducing the burden of manual data annotation.
3Adaptability or versatility
If unsupervised learning is performed on unlabeled data first, then the model learns general data distributions, but the model lacks specific text-conditioned generation capability initially
Solution Approach 1:
Unsupervised pre-training is performed as a preliminary action to learn general data distributions, followed by supervised fine-tuning to acquire text-conditioned generation capability. This sequence ensures the model has both general adaptability and specific controllability.
Solution Approach 2:
The system merges unsupervised learning and supervised fine-tuning into a unified two-stage training framework. The unsupervised stage learns general patterns while the supervised stage learns text-conditioned generation, and combining both stages produces a model with comprehensive capabilities.
Data Source
AI summary
Embodiments described herein provide a diffusion-based framework that is trained on a dataset with limited text labels, to generate a distribution of data samples in the dataset given a specific text description label. Specifically, firstly, unlabeled data is used to train the diffusion model to generate a data distribution of data samples given a specific text description label. Then text-labeled data samples are used to finetune the diffusion model to generate data distribution given a specific text description label, thus enhancing controllability of training.