Task-agnostic continual learning memory pool management

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Solution Overview

Problem

Existing task-agnostic continual learning systems are limited by their ability to handle only a pre-defined number of classes and tasks, lacking the capability to adapt to an unlimited number of tasks and classes.

Innovation Solution

The implementation of a task-agnostic continual learning system that utilizes a memory and metadata handling mechanism, allowing for the learning of an unlimited number of classes through the management of the model's memory pool and metadata, enabling dynamic model updates and improved memory utilization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If a pre-defined number of classes and tasks are handled in traditional continual learning systems, then the system structure remains simple and manageable, but the system cannot adapt to an unlimited number of tasks and classes

Engineering Contradiction:

Improvecapability to handle unlimited tasks and classesVSAvoidsystem structure complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system segments the ensemble memory into multiple manageable components, each handling specific tasks or classes. This segmentation allows the system to manage unlimited tasks by dividing them into organized segments rather than treating them as a monolithic structure, resolving the contradiction between adaptability and complexity.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system implements dynamic memory management where the ensemble memory can be dynamically updated, expanded, and reorganized as new tasks and classes are introduced. This dynamic approach enables the system to adapt to unlimited tasks while maintaining manageable complexity through flexible resource allocation and memory management strategies.

Inventive Principle:

Principle #15Dynamics

2Adaptability or versatility

If the system is restarted to handle new tasks, then the system can reset and reinitialize for new task sets, but this causes loss of time and interrupts continuous operation

Engineering Contradiction:

Improveability to handle new tasksVSAvoidsystem restart time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system maintains continuous operation by implementing incremental learning capabilities that allow new tasks and classes to be integrated without system restart. The ensemble memory is dynamically updated with new information while preserving existing knowledge, enabling continuous adaptation and eliminating time losses associated with system reinitialization.

Inventive Principle:

Principle #20Continuity of useful action

Solution Approach 2:

The system performs preliminary organization and allocation of memory resources in advance, preparing the ensemble memory structure to accommodate future tasks and classes. This preliminary setup enables seamless integration of new tasks without requiring system restart, as the infrastructure is already in place to handle expanding task requirements.

Inventive Principle:

Principle #10Preliminary action

3Reliability

If memory is allocated for each task and class, then the system can store and retrieve task-specific information, but memory utilization becomes inefficient and resources are wasted

Engineering Contradiction:

Improvetask information storage accuracyVSAvoidmemory resource usage

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The system implements a universal ensemble memory structure that serves multiple tasks and classes simultaneously rather than allocating dedicated memory for each. This multi-functional memory system dynamically adapts to store and retrieve information for different tasks, improving memory utilization efficiency while maintaining the reliability needed for accurate task-specific information storage.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system merges memory resources into a shared ensemble memory that combines storage capabilities for multiple tasks and classes. By combining previously separate memory allocations into a unified structure with intelligent management, the system achieves efficient resource utilization while preserving the ability to reliably store and retrieve task-specific information through selective access mechanisms.

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS20250124300A1Task-agnostic continual learning of systems performance by efficiently managing ensemble memory

Publication Date: 2025.04.17 DELL PROD LP

AI summary

Techniques for enabling a task-agnostic continual learning (CL) system to handle an unlimited number of tasks and/or classes are disclosed. The weights of a pre-trained encoder are frozen. A memory pool of models/classifiers is accessed. The pre-trained encoder encodes an input. The encoded data is used to obtain a top-k nearest set of models from the memory pool. These models operate using the encoded data. Their output is decoded into a sparse matrix, which is then aggregated. The top-k nearest set of models are then updated based on the aggregation. In doing so, the CL system is now able to handle a new class or a new task.