Continual-Learning Neural Control Using Pseudo-Data Replay
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Solution Overview
Problem
Artificial neural networks (ANNs) face the challenge of catastrophic forgetting, where they tend to forget previously learned information when adapting to new data, making it difficult to build upon existing models without losing previously acquired knowledge.
Innovation Solution
A control system employing two artificial neural networks (ANNs) and iterative sampling to generate pseudo-data samples, allowing the first ANN to learn new classes while preserving knowledge from the second ANN, which acts as a memory model, thereby minimizing forgetting through knowledge distillation and consolidation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the ANN is trained to learn new information, then the ANN can adapt to new classes, but the ANN forgets previously learned information (catastrophic forgetting)
Solution Approach 1:
The patent applies preliminary action by generating pseudo-data samples from previously learned classes before training on new classes. This is done through iterative sampling that creates modified base data samples by moving them towards decision boundaries, then using these samples to train the ANN on new classes while preserving knowledge of old classes.
Solution Approach 2:
The patent uses pseudo-data samples as an intermediary between old and new classes. These synthetic samples act as a bridge that allows the ANN to learn new classes without directly conflicting with previously learned information, thereby reducing catastrophic forgetting while maintaining adaptability to new classes.
2Reliability
If all historic training data is stored in a memory buffer, then the ANN can be trained with mixed historic and new data, but the system requires significant memory resources
Solution Approach 1:
The patent creates copies of base data samples through iterative sampling to generate pseudo-data samples. Instead of storing all historic training data, the system stores a limited set of base data samples and generates additional training samples on-demand by iteratively modifying these base samples towards decision boundaries, thereby reducing memory requirements while maintaining training effectiveness.
Solution Approach 2:
The patent changes parameters of base data samples through iterative sampling, creating modified versions that move towards decision boundaries. This allows the system to generate diverse training samples from a small set of base samples, reducing the need to store large quantities of historic data while still providing sufficient training variation.
Data Source
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AI summary
The present disclosure relates to a control system comprising: - a computation device (301) implementing at least a first artificial neural network (Net_1); - one or more sensors (320) configured to capture input data samples; and - one or more actuators (322); wherein the computation device (301) is configured to: generate at least one pseudo-data sample by: a) iterative sampling one of the base data samples towards one of the decision boundaries to generate one or more modified base data samples; and b) selecting one or more of the modified base data samples to form the pseudo-data sample.