Experience Replay Encoder for Continual Learning
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Solution Overview
Problem
Existing learning systems face challenges in continual learning as they tend to forget previously learned tasks when adapting to new tasks, leading to decreased learning capacity and performance over time, particularly due to catastrophic forgetting issues.
Innovation Solution
A system that uses experience replay with a Generative Autoencoder to map tasks into a shared distribution in an embedding space, generating pseudo-data points for experience replay, allowing the system to adapt to new tasks without forgetting past knowledge by enforcing a shared distribution across tasks using sliced-Wasserstein distance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If model consolidation is used to separate information pathways for different tasks, then catastrophic forgetting is reduced, but network learning capacity is compromised
Solution Approach 1:
The network is segmented into task-specific pathways and a shared pathway. Task-specific pathways preserve knowledge for individual tasks while the shared pathway enables generalization. This segmentation allows the network to maintain performance on past tasks without compromising overall learning capacity.
Solution Approach 2:
The architecture implements nesting by placing task-specific modules within a broader shared network structure. Each task-specific pathway is nested within the shared pathway, allowing hierarchical organization of knowledge where specific task knowledge is contained within the general learning framework.
2Reliability
If experience replay with memory buffer is used to store past experiences, then catastrophic forgetting is reduced, but system complexity and memory requirements increase
Solution Approach 1:
The invention extracts the essential function of experience replay by removing the need for explicit memory buffers. Instead of storing and replaying actual past experiences, the system uses task-specific pathways that inherently preserve task distributions, extracting only the necessary memory function while eliminating buffer complexity.
Solution Approach 2:
The system creates copies of task-specific knowledge through separate pathways rather than copying actual data samples into a buffer. Each task-specific pathway maintains a representation of its task distribution, effectively copying the essential information without requiring storage of original experiences.
3Adaptability or versatility
If generative models are used to produce pseudo-data points, then dependence on memory buffer is reduced, but mode collapse and hyper-parameter sensitivity increase
Solution Approach 1:
Task-specific pathways act as intermediaries between the shared network and the task distributions. Rather than using complex generative models to produce pseudo-data, the task-specific pathways directly maintain and provide task representations, serving as a stable intermediary that eliminates mode collapse issues.
Solution Approach 2:
Each task-specific pathway serves itself by maintaining its own task distribution representation. The pathways are self-sufficient in preserving task knowledge without requiring external generative models or memory buffers, making the system more stable and less sensitive to hyper-parameters.
Data Source
AI summary
Described is a system for continual learning using experience replay. In operation, the system receives a plurality of tasks sequentially, from which a current task is fed to an encoder. The current task has data points associated with the current task. The encoder then maps the data points into an embedding space, which reflects the data points as discriminative features. A decoder then generates pseudo-data points from the discriminative features, which are provided back to the encoder. The discriminative features are updated in the embedding space based on the pseudo-data points. The encoder then learns (updates) a classification of a new task by matching the new task with the discriminative features in the embedding space.