Ridge Regression Cost Penalty for Rapid Class Augmentation
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Solution Overview
Problem
Existing incremental learning algorithms face challenges in efficiently learning new classes without experiencing catastrophic forgetting of prior classes, especially in low sample support environments.
Innovation Solution
The Ridge Regression for Rapid Class Augmentation (R3CA) algorithm uses a ridge regression penalty for regularization and a single head incremental classifier to incrementally learn new classes while preserving performance on prior classes, employing a frozen, pretrained feature extraction backbone.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If standard optimizers are used to learn new classes incrementally, then the model adapts to new data quickly, but catastrophic forgetting occurs and performance on prior classes degrades
Solution Approach 1:
The patent pre-computes and stores the inverse feature covariance matrix before incremental learning begins. This preliminary action creates a computational foundation that enables rapid adaptation to new classes while preserving knowledge of prior classes through ridge regression regularization, eliminating catastrophic forgetting without requiring retraining on all data.
Solution Approach 2:
The patent modifies the optimization approach by incorporating ridge regression with a regularization parameter lambda into the incremental learning process. This parameter change transforms the standard optimizer into a regularized optimizer that balances adapting to new classes with maintaining performance on prior classes, preventing catastrophic forgetting through controlled weight updates.
2Reliability
If replay or rehearsal methods are used to preserve prior class performance, then catastrophic forgetting is mitigated, but memory requirements grow continuously and inference time increases
Solution Approach 1:
The patent extracts the essential statistical properties (inverse feature covariance matrix) from the training data and stores only this compressed representation instead of retaining all raw training samples. This extraction eliminates the need for growing memory frameworks while preserving the ability to maintain performance on prior classes through ridge regression regularization.
Solution Approach 2:
The patent creates a mathematical copy (inverse feature covariance matrix) that captures the essential relationships in the training data without requiring storage of the actual data samples. This copy enables the model to maintain prior class performance through regularization while using minimal memory, avoiding the continuous memory growth problem of replay methods.
3Reliability
If replay methods with nearest neighbor classification are used, then prior class knowledge is preserved, but prediction time becomes significantly slower
Solution Approach 1:
The patent replaces the mechanical nearest neighbor classification process with a direct matrix multiplication operation using the pre-computed inverse feature covariance matrix. This substitution eliminates the need for computationally intensive distance calculations during inference, maintaining fast prediction speeds while preserving prior class performance through ridge regression regularization.
4Reliability
If full retraining on all data is performed to learn new classes, then performance on all classes is maintained, but computational cost and training time become infeasible
Solution Approach 1:
The patent pre-computes the inverse feature covariance matrix from the training data before incremental learning begins. This preliminary computation enables subsequent rapid adaptation to new classes through efficient ridge regression updates without requiring retraining on all data, maintaining high training efficiency while preserving performance on all classes.
Solution Approach 2:
The patent changes the training approach from full retraining to incremental ridge regression updates with regularization. This parameter change in the optimization strategy allows the model to adapt to new classes efficiently while maintaining performance on prior classes through controlled weight updates, achieving feasible computational costs for continuous learning.
Data Source
AI summary
Ridge Regression for Rapid Class Augmentation (R3CA), a regularized version of the XRCA incremental learning algorithm, implements an unconstrained, recursive least-squares (RLS) style of optimization that incorporates knowledge of all the past training examples into each optimization step by recursively computing an IFCM in a single multi-class prediction head. The single multi-class prediction head receives class token feature vectors from a pretrained, self-supervised, vision transformer model and is able to achieve the same optimal performance as a non-incrementally trained classifier in a jointly optimal manner over a set of increasing classes. R3CA excels at low sample incremental learning applications.