Neural Style Transfer With Latent Embeddings for Lower Memory Use
Find Innovative SolutionsGenerate Solutions
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.
Innovation Solution
A variational autoencoder is trained to convert content features into a lower-dimensional latent embedding space, allowing for efficient generation of high-resolution style transfer results by optimizing content and style attributes using losses.
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 and the number of usable style images is restricted
Solution Approach 1:
The patent extracts only the essential style characteristics from style images and stores them in a compact feature database, rather than storing all features. This extraction process identifies and retains only the most relevant style attributes needed for high-quality style transfer, significantly reducing memory requirements while maintaining transfer quality.
Solution Approach 2:
The patent changes the parameter representation of style features by using a compressed feature space with reduced dimensionality. Instead of storing complete high-resolution feature maps, the system transforms features into a condensed representation that captures essential style information with fewer parameters, making large-scale style transfer feasible.
2Adaptability or versatility
If all features from style images are stored in memory, then more style images can be used, but the latency of nearest neighbor search increases with the number of features
Solution Approach 1:
The patent extracts and stores only essential style features in a compressed format, enabling the system to handle a larger number of style images without proportionally increasing search latency. The compact feature representation allows for faster comparison and matching operations.
Solution Approach 2:
By transforming the feature representation into a lower-dimensional space, the patent reduces the computational complexity of nearest neighbor searches. This parameter transformation maintains the ability to distinguish between different styles while significantly decreasing the time required for feature matching.
3Ease of manufacture
If features are stored in memory for nearest neighbor search, then style transfer can be performed, but data augmentation and high-resolution processing are restricted
Solution Approach 1:
The patent extracts style features in a resolution-agnostic manner, storing only the essential style characteristics that are independent of image resolution. This extraction approach enables the system to apply the same style features to content images at various resolutions without being constrained by memory limitations.
Solution Approach 2:
The patent creates a universal style feature representation that can be applied across multiple scenarios including different resolutions, data augmentation operations, and various style transfer tasks. The compressed feature database serves as a multi-functional resource that supports diverse style transfer applications without requiring separate storage for each scenario.
Data Source
AI summary
One embodiment of the present invention sets forth a technique for performing style transfer. The technique includes converting, via a trained variational autoencoder, a first set of features associated with a content sample into a second set of features from a feature space associated with one or more style samples. The technique also includes computing one or more losses based on the first set of features and the second set of features. The technique further includes generating a style transfer result based on the content sample and the one or more losses, where the style transfer result includes one or more content-based attributes of the content sample and one or more style-based attributes of the style sample.