Personalized Digital Image Aesthetics via Segmented Model Offsets
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Solution Overview
Problem
Conventional techniques for estimating digital image aesthetics lack accuracy in addressing the diverse visual preferences of users, leading to inefficiencies in image curation and search functionalities.
Innovation Solution
A personalized offset is generated to adapt a generic model for digital image aesthetics, allowing for the creation of a personalized aesthetics score by leveraging a generic model trained with a large dataset and a smaller personal training dataset, thereby conserving computational resources and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a generic model is used to estimate digital image aesthetics, then computational resources are conserved and the system is simple to operate, but measurement precision and reliability are insufficient due to inability to address diverse user preferences
Solution Approach 1:
The aesthetics estimation system is segmented into two distinct models: a generic model that provides baseline aesthetics scores and a personalized model that captures individual user preferences. This segmentation allows the system to maintain simplicity through the generic model while improving accuracy through the personalized model specific to each user's aesthetic preferences.
Solution Approach 2:
The generic model is trained in advance on a large dataset of images and aesthetics scores from multiple users, establishing a preliminary baseline for aesthetics estimation. This preliminary action enables the system to provide reasonable aesthetics scores without requiring extensive computational resources at runtime, while personalized models can be subsequently trained to refine accuracy for individual users.
2Measurement precision
If a personalized model is trained for each user to improve aesthetics score accuracy, then measurement precision improves, but computational resources and device complexity increase
Solution Approach 1:
The generic model serves as an intermediary between the large dataset of user preferences and individual personalized models. It captures general aesthetic principles from diverse user feedback and provides a foundation that personalized models can build upon, reducing the complexity of training personalized models from scratch while still achieving high accuracy for individual users.
Solution Approach 2:
The system transitions from a single generic model with fixed parameters to a family of personalized models where parameters are adapted for each user based on their specific preferences. This parameter adaptation allows the system to maintain a manageable complexity structure while achieving high measurement precision for individual users through customized model parameters.
3Adaptability or versatility
If a personalized model is trained for each user, then adaptability to individual preferences improves, but loss of time and computational resources increase due to training multiple models
Solution Approach 1:
The generic model is trained in advance on a comprehensive dataset, performing preliminary action to capture general aesthetic patterns. This allows personalized models to be trained more quickly by building upon the pre-trained generic model rather than training from scratch, reducing the time loss associated with personalization while maintaining high adaptability to individual user preferences.
Solution Approach 2:
The generic model serves as a template or copy that can be efficiently replicated and adapted for individual users. Instead of creating entirely new personalized models, the system copies the generic model structure and adapts its parameters to individual user preferences, significantly reducing training time and computational resources while maintaining high adaptability.
Data Source
AI summary
Techniques and systems are described to determine personalized digital image aesthetics in a digital medium environment. In one example, a personalized offset is generated to adapt a generic model for digital image aesthetics. A generic model, once trained, is used to generate training aesthetics scores from a personal training data set that corresponds to an entity, e.g., a particular user, group of users, and so on. The image aesthetics system then generates residual scores (e.g., offsets) as a difference between the training aesthetics score and the personal aesthetics score for the personal training digital images. The image aesthetics system then employs machine learning to train a personalized model to predict the residual scores as a personalized offset using the residual scores and personal training digital images.