Domain Invariant Regularization for Image Classification Generalization
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Solution Overview
Problem
Machine learning models for image classification face challenges in generalizing across domains due to domain shift issues, where models perform poorly when classifying images from new target domains with style discrepancies, and existing techniques like domain adaptation and generalization struggle with diverse source domains.
Innovation Solution
The use of domain invariant regularization techniques, where training images with disentangled content-specific and domain-specific feature spaces are used to train models to be invariant to domain-specific features, achieved by generating perturbed images using a generative model that combines content-specific features with randomly selected style-specific features, allowing the model to compute similar outputs for images with the same content but different styles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If domain adaptation technique uses data from target domain to train the model, then the model learns domain invariant features for classification, but the model cannot be used to classify images from domains that are not available during training
Solution Approach 1:
The patent segments the feature space into domain-specific features and content-specific features using disentangled representation learning. By separating these features, the model can learn from source domains without memorizing target domain characteristics, enabling generalization to unseen domains while maintaining classification accuracy.
Solution Approach 2:
The patent applies domain invariant regularization during the training phase on source domains to pre-establish domain invariance in the feature representations. This preliminary action ensures that when the model encounters unseen target domains, it already possesses the invariance needed for accurate classification without requiring target domain data.
2Adaptability or versatility
If domain generalization technique trains using diverse source domains, then the model attempts to learn domain invariant features, but training becomes more difficult as source domains become more diverse due to domain-specific information interfering with domain invariant features
Solution Approach 1:
The patent extracts and removes domain-specific features from the training data through disentangled representation learning and domain invariant regularization. By taking out the interfering domain-specific information, the model can focus on learning domain-invariant content features, simplifying the training process even with diverse source domains.
Solution Approach 2:
The patent introduces domain invariant regularization as an intermediary mechanism that mediates between diverse source domains and the model learning process. This regularizer acts as a bridge that filters out domain-specific variations and emphasizes domain-invariant patterns, making training with diverse domains more manageable.
3Reliability
If the model is trained on source domains with domain-specific features such as styles, lighting conditions, and textures, then the model performs well on images from the same domains, but it performs poorly when used to classify images from new target domains due to domain shift
Solution Approach 1:
The patent changes the parameter optimization objective by adding domain invariant regularization terms to the loss function. This modifies the training parameters to minimize both classification error and domain-specific feature variations, enabling the model to maintain high accuracy on source domains while improving generalization to target domains.
Solution Approach 2:
The patent creates a multi-functional feature representation that serves both domain-specific classification (maintaining accuracy on source domains) and domain-invariant generalization (performing well on target domains). The disentangled features enable the model to selectively use appropriate features for different purposes.
Data Source
AI summary
A system and a method are disclosed for receiving an input image, using a domain invariant machine learning model to compute an output based on the input image, wherein the domain invariant machine learning model is trained using domain invariant regularization, and displaying information based on the output.