Joint Probability Estimation for Diffusion-Based Image Annotation
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Solution Overview
Problem
Manual annotation of images for training imaging modules is time-intensive and inefficient, necessitating an improved method for training imaging systems and obtaining relative probabilities between images and annotations.
Innovation Solution
A computer-implemented method using a denoiser neural network to perform forward diffusion on image and annotation pairs, clean noisy distributions, and determine a joint probability based on denoiser loss values, with optional responses and adaptations to improve annotation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotations are used for training imaging modules, then training accuracy can be achieved, but the process becomes time-intensive and inefficient
Solution Approach 1:
The patent uses diffusion models to generate synthetic annotated images that copy the structure and content of real annotated images. These synthetic images serve as training data, replacing the need for manual annotation while maintaining training effectiveness. The system generates unlimited synthetic training pairs automatically, eliminating time-intensive manual work.
Solution Approach 2:
The system employs self-supervised learning where the diffusion model generates its own training data from existing annotated images. The model learns to denoise and reconstruct images, automatically creating training pairs without human intervention. This self-service mechanism continuously generates training data, eliminating dependency on manual annotation processes.
2Reliability
If more manual annotations are created to improve training data quality, then model performance improves, but the cost and time investment increases
Solution Approach 1:
The diffusion model creates synthetic copies of annotated images with varying noise levels, generating unlimited training data from a limited set of original annotated images. These synthetic copies maintain the semantic content and annotation accuracy of the originals while providing diverse training examples, thus improving model performance without additional manual annotation effort.
Solution Approach 2:
The system varies noise parameters in the diffusion process to generate training images with different degradation levels. By changing noise variance and diffusion steps, the system creates a diverse set of training examples from single annotated images, improving model robustness and performance without requiring proportional increases in manual annotation volume.
3Measurement precision
If traditional diffusion models are used for image generation, then image quality can be achieved, but computational complexity and training time increase
Solution Approach 1:
The system pre-trains the diffusion model on a large dataset of natural images to learn general image priors and structures. This preliminary training establishes a strong foundation that reduces the complexity of subsequent fine-tuning tasks. The pre-trained model already understands image distributions, making the actual annotation generation process computationally more efficient.
Solution Approach 2:
The training process is divided into separate stages: pre-training on natural images, then fine-tuning on annotated image pairs. The diffusion process itself is segmented into forward diffusion (adding noise) and reverse diffusion (removing noise). This segmentation allows each component to be optimized independently, reducing overall computational complexity while maintaining image quality.
Data Source
AI summary
A computer-implemented method when executed by data processing hardware causes the data processing hardware to perform operations. The operations include training, based on a training distribution of a prediction model, a denoiser, the denoiser being a neural network, receiving an original distribution set including an image and image annotations, and executing, on the image and the image annotations, forward diffusion to define a noisy distribution set including a noisy image and noisy image annotations. The operations also include cleaning, by the trained denoiser, the noisy distribution set to define a cleaned distribution set including a cleaned image and cleaned image annotations, determining, based on a comparison of the cleaned distribution set with the original distribution set, a denoiser loss value, and generating, based on the denoiser loss value, a joint probability.