Input Feature Encoding for Robust Classifier Generalization
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Solution Overview
Problem
Conventional approaches to classifier generalization in machine learning fail to effectively minimize the difference between training and inference errors when the training and test sets are not representative of the empirical sample set, leading to unreliable decisions in real-world applications.
Innovation Solution
The method involves encoding original input features with a uniquely decodable code to build an encoded model, which learns a more general classification function that is robust to noise and adversarial perturbations, using tools from algorithmic information theory and coding theory to minimize the generalization error between training and inference errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional approaches minimize training-test error difference on identically distributed sets, then training accuracy is improved, but inference reliability on real-world corrupted samples deteriorates
Solution Approach 1:
The patent applies preliminary action by encoding input features before training the classifier. The encoder transforms raw input features into encoded features that capture intrinsic relationships and structures, preparing the data in advance to enhance both training accuracy and inference reliability on corrupted samples
Solution Approach 2:
The patent changes the parameter representation of input features by applying encoding transformations. The encoder modifies the feature space by transforming original features into encoded features with different statistical properties and relationships, allowing the classifier to learn more robust decision boundaries
2Adaptability or versatility
If domain-generalization trains on samples from different domains, then generalization to unknown domains is improved, but device complexity and training constraints increase
Solution Approach 1:
The patent introduces an encoder as an intermediary component between the input features and the classifier. This encoder acts as a mediator that transforms features into a representation space where the classifier can learn domain-invariant patterns, achieving generalization without requiring training on multiple domains
Solution Approach 2:
The patent segments the learning system into two distinct components: an encoder that processes input features and a classifier that makes predictions. This segmentation allows the encoder to learn robust feature representations that are independent of domain-specific variations, simplifying the training process
3Reliability
If data-augmentation performs label-preserving transformations, then classifier robustness is improved, but additional training data requirements increase
Solution Approach 1:
The patent replaces the mechanical approach of data augmentation (creating multiple transformed copies of training samples) with an encoding-based approach. The encoder transforms the feature representation space, achieving robustness through feature transformation rather than sample multiplication
Data Source
AI summary
Techniques for classifier generalization in a supervised learning process using input encoding are provided. In one aspect, a method for classification generalization includes: encoding original input features from at least one input sample {right arrow over (x)}S with a uniquely decodable code using an encoder E(⋅) to produce encoded input features E({right arrow over (x)}S), wherein the at least one input sample {right arrow over (x)}S comprises uncoded input features; feeding the uncoded input features and the encoded input features E({right arrow over (x)}S) to a base model to build an encoded model; and learning a classification function {tilde over (C)}E(⋅) using the encoded model, wherein the classification function {tilde over (C)}E(⋅) learned using the encoded model is more general than that learned using the uncoded input features alone.