Energy-Based Generative ConvNet for Single-Image Distribution Learning
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Solution Overview
Problem
Existing image processing systems require multiple training images to learn image distributions, making them challenging to train and less efficient for tasks like image generation and manipulation from a single image.
Innovation Solution
The proposed energy-based generative ConvNet framework learns internal statistics of a single natural image using a pyramid of energy functions parameterized by bottom-up deep neural networks, enabling efficient training and image generation without the need for auxiliary models or external data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If energy-based generative models are trained using multiple images, then the model can learn image distributions more accurately, but the training complexity and data requirements increase
Solution Approach 1:
The patent extracts and utilizes only the necessary statistical information from a single image through energy functions and bottom-up neural networks, eliminating the need for multiple training images while maintaining accurate distribution learning capability
Solution Approach 2:
The model performs self-learning from a single input image by automatically extracting internal statistics and generating diverse outputs, serving its own training needs without external data assistance
2Manufacturing precision
If traditional generative models are trained from multiple images, then generation quality improves, but the system becomes less efficient for single-image tasks
Solution Approach 1:
The patent changes the fundamental parameters of the system by using energy-based functions and bottom-up neural networks that can learn from a single image, enabling both high generation quality and training efficiency simultaneously
3Reliability
If auxiliary models are used to assist training, then training stability improves, but the system complexity increases
Solution Approach 1:
The energy-based generative model trains itself using only the input image data without requiring auxiliary models, achieving training stability through its own internal mechanisms while minimizing system complexity
Data Source
AI summary
Different from prior works that model the internal distribution of patches within an image implicitly with a top-down latent variable model (e.g., generator), embodiments explicitly represent the statistical distribution within a single image by using an energy-based generative framework, where a pyramid of energy functions, each parameterized by a bottom-up deep neural network, are used to capture the distributions of patches at different resolutions. Also, embodiments of a coarse-to-fine sequential training and sampling strategy are presented to train the model efficiently. Besides learning to generate random samples from white noise, embodiments can learn in parallel with a self-supervised task (e.g., recover an input image from its corrupted version), which can further improve the descriptive power of the learned model. Embodiments does not require an auxiliary model (e.g., discriminator) to assist the training, and embodiments also unify internal statistics learning and image generation in a single framework.