Implicit-Explicit Regularization for Continual Learning in Neural Networks
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Solution Overview
Problem
Continual learning in artificial neural networks faces challenges such as catastrophic forgetting and overfitting, especially in low-buffer regimes, where existing methods struggle to maintain generalizability and prevent prior information loss.
Innovation Solution
The method intertwines implicit and explicit regularization techniques, using supervised contrastive learning for generalizable feature learning and exponential moving averages to regularize the model's function space, aligning geometric structures within unit hyperspheres to enhance generalization and reduce overfitting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rehearsal-based methods maintain a bounded memory buffer to store and replay samples from previous tasks, then catastrophic forgetting is mitigated, but overfitting occurs and generalizability deteriorates in low-buffer regimes
Solution Approach 1:
The model is segmented into multiple components: a backbone network for feature extraction, a projection head for representation learning, and a linear classifier for task-specific prediction. This segmentation allows different parts of the system to serve different functions, with the projection head capturing generalizable representations while the classifier adapts to specific tasks, thereby reducing overfitting in low-buffer regimes
Solution Approach 2:
The invention introduces a projection head that maps features to a separate representation space, adding a dimensional transformation layer between feature extraction and classification. This additional dimension enables the model to learn invariant representations that generalize across tasks while maintaining task-specific discrimination capabilities
2Adaptability or versatility
If explicit regularization is applied in function space to limit input/output function changes between tasks, then generalization is improved, but model complexity increases
Solution Approach 1:
A projection head is introduced as an intermediary component between the backbone network and the linear classifier. This projection head serves as a mediator that transforms features into a representation space where task-invariant patterns can be captured, enabling explicit regularization in function space without directly complicating the main classification pathway
Solution Approach 2:
The projection head performs preliminary representation learning before the final classification stage. By pre-processing features through this intermediate layer with its own regularization constraints, the model prepares generalized representations in advance, reducing the complexity burden on the main classification function
Data Source
AI summary
A computer-implemented method for continual learning in deep neural networks that introduces robust inductive biases by intertwining implicit regularization, using a projection head through auxiliary contrastive representation learning, and explicit consistency regularization on the soft targets using exponential moving average. To further leverage the global relationship between representations learned, the method of the current invention comprises a regularization strategy of guiding the classifier towards the activation correlations in the unit hypersphere of the projection head. These implicit and explicit regularizations encourage the model to learn generalizable representations, thereby reducing task interference and catastrophic forgetting.