Multi-Module Cognitive Architecture for Continual Learning
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Solution Overview
Problem
Current deep neural networks (DNNs) face limitations in continual learning, particularly suffering from 'catastrophic forgetting' and failing to generalize to small changes in distribution, which restricts their ability to perform lifelong learning effectively, unlike humans who can acquire new skills while retaining previous knowledge.
Innovation Solution
A multi-module artificial cognitive architecture is proposed, comprising a first neural network module for explicit knowledge and a second module with multiple sub-modules for implicit knowledge, along with a memory buffer, where visual data samples are processed to learn and share knowledge representations, incorporating memory replay and inductive bias to enhance generalization and retention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If DNNs are trained on continuous data streams to learn new information, then adaptability improves, but previously learned knowledge is forgotten
Solution Approach 1:
The system segments the single DNN into multiple specialized modules: an explicit knowledge module for conscious learning, an implicit knowledge module for unconscious pattern recognition, and a memory buffer for storing past experiences. This segmentation allows each module to handle different aspects of learning independently, preventing interference between new and old knowledge while maintaining overall adaptability.
Solution Approach 2:
The memory buffer pre-stores data samples from previous tasks before new learning begins. During continual learning, the system retrieves and replays these stored samples alongside new data, ensuring that previously learned knowledge is actively reinforced before being potentially overwritten, thus preventing catastrophic forgetting in advance.
2Productivity
If DNNs focus on learning new tasks quickly, then learning speed improves, but generalization to small distribution changes deteriorates
Solution Approach 1:
The implicit knowledge module acts as an intermediary between the explicit knowledge module and the raw data. It processes data unconsciously to extract general patterns and inductive biases, which then guide the explicit module's learning. This intermediary layer enables faster learning by pre-processing data in a way that improves generalization without sacrificing learning speed.
Solution Approach 2:
The system dynamically adjusts the balance between learning new tasks and retaining old knowledge by modifying the replay ratio parameter. By controlling what proportion of training samples come from the memory buffer versus new data, the system can adapt its behavior to prioritize either learning speed or generalization depending on the current task requirements.
Data Source
AI summary
A computer-implemented method for continual task learning in an artificial cognitive architecture that includes a first neural network module for encoding explicit knowledge representations, a second neural network module for encoding implicit knowledge representations, and a memory buffer. A visual data stream is provided to the architecture. Visual data samples are stored from said visual data stream in the memory buffer. Both visual data samples of the visual data stream and visual data samples from the memory buffer are processed using the first neural network module for learning explicit knowledge representations. Both samples of said visual data stream and visual data samples from the memory buffer are processed using the second neural network module for learning implicit knowledge representations. Information is transformed and shared between the first neural network module and the second neural network module, such as learned knowledge representations, stored within the second neural network module into the first neural network module, as well as transforming and sharing information between sub-modules of the second neural network.