Neural Network Image Generation Using Energy-Based Models
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Solution Overview
Problem
Current generative models face challenges in controllable and compositional image generation, particularly due to high computational costs and difficulties in introducing new attributes, which limits their applicability in tasks like data bias reduction and anomaly detection.
Innovation Solution
The use of energy-based models (EBMs) in a latent space of pre-trained generative models, combined with ordinary differential equation (ODE) solvers, allows for efficient and robust sampling, enabling controllable and compositional image generation by training an attribute classifier and combining energy functions to form new image generators without retraining from scratch.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a conditional model is trained from scratch for new attributes, then controllable generation with specific attributes is achieved, but computational cost becomes excessively high
Solution Approach 1:
The patent pre-trains a generative model on a large dataset to learn the underlying data distribution and features. This preliminary training establishes a foundation that can be later adapted to new attributes through energy-based model formulation, avoiding the need to retrain from scratch for each new attribute set.
Solution Approach 2:
The patent formulates controllable generation as an energy-based model where different attribute combinations correspond to different energy functions. By changing the parameters (energy functions) rather than retraining the entire model, the system can efficiently adapt to new attributes with minimal computational cost.
2Reliability
If existing generative models are used for compositional generation, then image generation capability is maintained, but performance deteriorates on rare combinations of attributes
Solution Approach 1:
The patent creates a universal energy-based model framework that can handle multiple attributes and their combinations through a single unified formulation. The model learns to represent various attribute combinations (including rare ones) by formulating them as energy minimization problems, making the system versatile for compositional generation while maintaining reliable image quality.
3Productivity
If training time is reduced for faster deployment, then productivity increases, but model performance and image quality may deteriorate
Solution Approach 1:
The patent extracts the essential generative capabilities into a pre-trained base model and separates the attribute-specific control into energy-based formulations. This extraction allows the bulk of the training to be done once on large datasets, while subsequent adaptation to new attributes requires minimal training time while preserving image quality through the energy-based control mechanism.
Data Source
AI summary
Apparatuses, systems, and techniques are presented to generate one or more images comprising one or more objects based, at least in part, on one or more dynamically configurable attributes of the one or objects. In at least one embodiment, one or more images comprising one or more objects can be generated based, at least in part, on one or more dynamically configurable attributes of the one or objects.