Neural Network Preference Prediction for Image Attribute Control
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Solution Overview
Problem
Current deep generative models lack controllability in modifying relative attributes of generated images, such as 'sad' or 'tired' expressions, as users find it difficult to quantify these attributes due to the absence of a common scale, limiting their utility to easily quantifiable attributes.
Innovation Solution
A system that uses a neural network to predict user preferences by receiving feedback on attribute modifications, mapping similar preferences within a latent space, and iteratively generating images until the desired attribute meets the user's preference, allowing for precise adjustment of relative attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If current deep generative models use supervised learning or pre-trained classifiers to form mappings from attributes to latent space, then the models can generate images with controlled attributes, but the user interface becomes ill-suited for modifying relative attributes that lack a common scale (e.g., 'sad' or 'tired' expressions)
Solution Approach 1:
The system implements a feedback loop where users provide pairwise comparisons of generated images, and the model iteratively adjusts attribute values based on this feedback. The preference prediction module learns from user preferences to automatically determine appropriate attribute modifications, eliminating the need for users to manually quantify subjective attributes.
Solution Approach 2:
The patent introduces a preference prediction module as an intermediary between the user interface and the generative model. This module translates subjective user preferences into quantitative attribute adjustments, serving as a mediator that bridges the gap between qualitative user intent and quantitative model parameters.
2Measurement precision
If users must explicitly tune quantitative attributes to precisely modify image intensity, then attribute modification precision can be achieved, but the complexity of operation increases significantly
Solution Approach 1:
The system performs self-adjustment by automatically tuning attribute values based on learned user preferences. The preference prediction module autonomously determines the appropriate attribute modifications without requiring users to manually specify quantitative values, making the system self-serve the precision requirement.
Solution Approach 2:
The system dynamically changes attribute parameters based on feedback from user preferences. Instead of requiring users to set fixed parameter values, the model iteratively adjusts attribute parameters (such as emotion intensity) based on the preference prediction module's interpretation of user feedback, achieving precision through adaptive parameter changes.
3Measurement precision
If the system iteratively generates multiple sets of items based on user feedback, then the accuracy of attribute prediction improves, but the time required for generation increases
Solution Approach 1:
The preference prediction module is pre-trained on user preference data before actual image generation. This preliminary training allows the system to quickly predict user preferences during the iterative generation process without requiring extensive real-time computation, reducing the time penalty of iterative refinement.
Solution Approach 2:
The system performs a limited number of iterative generations rather than exhaustive optimization. By performing partial iterations (typically 3-5 rounds), the system achieves sufficient prediction accuracy without the excessive time cost of complete convergence, balancing precision and efficiency.
Data Source
AI summary
Systems and methods for guiding the generation of items based on a user preference. The system comprises a computing device comprising one or more processors, a neural network a transceiver, and at least one memory in communication with the computing device, the neural network, and the transceiver and storing computer program code. The system is configured to output a first set of items having a first attribute. The system may receive a first user input to generate, using the neural network, to generate one or more additional set of items (e.g., images) based on user preference. The system is configured to adjust and generate one or more new items until the at least one modified first attribute meets a desired preference.