Landscape Painting Generation Model With Interactive Distillation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing landscape painting generation models using CycleGAN for unpaired data produce unstable results with artifacts and noise, while Pix2pix fails to decouple style and content and requires significant memory usage.
Innovation Solution
A landscape painting generation model is developed using a StyleGAN-based generative adversarial network with adaptive instance normalization (AdaIN) for style transfer, followed by a lightweight student network trained through interactive distillation with feature constraints to achieve efficient and accurate landscape painting generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If CycleGAN is used for unpaired data style transfer, then style and content can be separated, but generation stability deteriorates with artifacts and noise
Solution Approach 1:
The patent introduces an intermediary teacher model (pre-trained StyleGAN) that provides stable style guidance without directly training on unpaired data. The student model learns from the teacher's intermediate representations, filtering out artifacts and noise while maintaining style-content separation capability.
Solution Approach 2:
The patent uses a student-teacher architecture where the student model copies useful features from the pre-trained teacher model. The teacher model generates intermediate representations that capture stable style characteristics, which the student model then uses to generate final outputs, avoiding the instability of direct unpaired data training.
2Reliability
If Pix2pix is used for paired data style transfer, then generation stability improves, but style and content decoupling fails and memory usage increases
Solution Approach 1:
The patent segments the generation process into distinct stages: style extraction from the teacher model, content preservation from input images, and synthesis by the student model. This segmentation allows independent optimization of style and content, achieving both stability and decoupling.
Solution Approach 2:
The pre-trained teacher model acts as an intermediary that provides stable style representations without requiring paired data. The student model uses these intermediate style features to maintain stability while achieving style-content decoupling through separate feature extraction paths.
3Reliability
If Pix2pix model is used, then generation stability improves, but memory usage increases and inference speed needs improvement
Solution Approach 1:
The student model copies only the essential style transfer capabilities from the large teacher model, discarding unnecessary components. This selective copying results in a compact student model that maintains stability while reducing memory requirements and improving inference speed.
Solution Approach 2:
The patent uses a smaller student model that can be deployed efficiently, replacing the need for large Pix2pix models. The student model achieves sufficient performance with fewer resources, acting as a cost-effective alternative to memory-intensive architectures.
Data Source
AI summary
A method includes: based on a generative adversarial network, constructing and training an initial network; constructing a teacher network and a student network using the initial network; inputting landscape painting training samples into the teacher network and the student network for feature extraction to obtain multiple first predicted feature maps and multiple intermediate feature maps output by the student network, and multiple second predicted feature maps and multiple interactive feature maps output by the teacher network; wherein the interactive feature maps are obtained by inputting the intermediate feature maps extracted by the student network at different stages into the teacher network; based on feature constraints between the first predicted feature maps and the second predicted feature maps, and feature constraints between the second predicted feature maps and each of the interactive feature maps, calculating training losses; and adjusting parameters of the student network based on the training losses.