Image Translation with Conditioning Vectors for Interpretable Control
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Solution Overview
Problem
Existing image transformation models lack fine-grained control over specific interpretable characteristics and require paired training data, limiting their ability to perform multiple transformations.
Innovation Solution
A machine-learned adversarial network using user-specified conditioning vectors to perform parameterized image transformations, allowing continuous control over defined characteristics without paired training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If machine-learned models are used to perform image transformations, then image transformation capability is improved, but fine-grained control over specific interpretable characteristics is lost
Solution Approach 1:
The patent introduces a conditioning vector as an additional parameter input to the generator model, allowing users to specify desired values for image characteristics (e.g., season, time of day, weather). This parameter change enables fine-grained control over transformation outcomes while maintaining automated image transformation capability through the machine-learned model.
2Reliability
If traditional image transformation models are used, then single-type transformations are achieved, but adaptability to multiple transformation types is limited
Solution Approach 1:
The patent designs a universal generator model that can perform multiple types of image transformations (seasonal changes, time of day changes, weather changes) through a single architecture. The conditioning vector allows the same model to adapt to different transformation types by simply changing the parameter values, eliminating the need for separate models for each transformation type.
3Measurement precision
If fine-grained control over transformation degree is implemented, then user control precision is improved, but model complexity increases
Solution Approach 1:
The patent introduces a conditioning vector as an intermediary between user input and the generator model. This vector serves as a mediator that carries user-specified desired values for image characteristics to the model, enabling fine-grained control without requiring complex model architecture changes. The conditioning vector simplifies the control interface while maintaining precision.
Data Source
AI summary
1. A computer-implemented method to perform image-to-image translation. The method can include obtaining one or more machine-learned generator models. The one or more machine-learned generator models can be configured to receive an input image and a user-specified conditioning vector that parameterizes one or more desired values for one or more defined characteristics of an output image. The one or more machine-learned generator models can be configured to perform, based at least in part on the user-specified conditioning vector, one or more transformations on the input image to generate the output image with the one or more desired values for the one or more defined characteristics. The method can include receiving the input image and the user-specified conditioning vector. The method can include generating, using the machine-learned generator model, an output image having the one or more desired values for the one or more characteristics.