Semi-Supervised Style Transfer for High-Resolution Memory Limits
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Solution Overview
Problem
Existing neural style transfer techniques face memory limitations and increased latency due to storing all features from style images, restricting the number of style images usable and impacting the quality and speed of high-resolution style transfer, especially in applications like movie production.
Innovation Solution
A semi-supervised training approach using a neural network with supervised and unsupervised losses to generate style transfer results, allowing high-resolution outputs efficiently by training on a limited number of paired input and output key frames, and applying style-based losses to remaining frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If all features from style images are stored in memory to perform nearest neighbor search, then style transfer quality is improved, but memory usage becomes infeasible at higher resolutions
Solution Approach 1:
The patent extracts only the essential style features needed for transfer rather than storing all features from style images. The system identifies and stores only the critical style attributes in a compact representation, eliminating the need to maintain complete feature sets in memory while preserving style transfer quality.
Solution Approach 2:
Instead of storing style features and searching for content matches (traditional approach), the patent inverts the approach by storing content features and searching for style matches. This inversion allows the system to work with smaller, more manageable data structures that fit within memory constraints at high resolutions.
2Adaptability or versatility
If features from multiple style images are stored to increase style variety, then adaptability is improved, but memory usage and search latency increase
Solution Approach 1:
The patent merges multiple style representations into a unified, compact style space. By combining style information from multiple images into a consolidated feature representation, the system maintains access to diverse styles while reducing the overall data volume and search time required to retrieve appropriate style features.
Solution Approach 2:
The patent transforms style features into a parameterized representation that captures essential style characteristics in a condensed form. By changing the parameterization of style data from raw pixel/feature values to compact style descriptors, the system achieves both style variety and efficient retrieval with reduced latency.
3Manufacturing precision
If high-resolution content images are processed to maintain image quality, then manufacturing precision is improved, but computational complexity and memory requirements increase
Solution Approach 1:
The patent segments the high-resolution image processing task into distinct stages: feature extraction at full resolution, style matching and transfer at reduced resolution, and final reconstruction. This segmentation allows the system to maintain image quality where it matters while performing computationally intensive operations at lower resolutions, reducing overall complexity.
Solution Approach 2:
The patent transitions from operating entirely in the spatial dimension (pixel space) to incorporating a style feature dimension. By projecting the problem into this additional dimension, the system can perform style transfer operations that are resolution-independent, reducing computational complexity while preserving the ability to generate high-resolution output.
Data Source
AI summary
One embodiment of the present invention sets forth a technique for performing style transfer. The technique includes training a neural network based on (i) one or more supervised losses computed between a first set of training output produced by the neural network from a first set of training content samples and a set of stylized samples corresponding to the first set of training content samples, and (ii) one or more unsupervised losses computed using a second set of training output produced by the neural network from a second set of training content samples to generate a trained neural network. The technique also includes inputting a content sample into the trained neural network, and generating, via execution of the trained neural network, a style transfer result that comprises one or more content-based attributes of the content sample and one or more style-based attributes of the set of stylized samples.