Bayesian Few-Shot Learning for Multi-Domain Adaptation
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Solution Overview
Problem
Current deep learning technologies face challenges in integrating multi-domain online learning and few-shot learning, particularly due to the phenomenon of forgetting past domains when learning new ones, with no existing method effectively combining these approaches.
Innovation Solution
The method integrates multi-domain-based online learning and few-shot learning by estimating domain and task context information, modulating initial model parameters, normalizing them, adapting to support data, calculating task execution and contrast losses, and updating model parameters using a Bayesian neural network framework.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multi-domain online learning is performed sequentially, then new domains can be learned efficiently, but the model forgets past domains
Solution Approach 1:
The patent applies preliminary action by pre-modulating model parameters based on domain and task information before actual learning occurs. The modulation information is acquired in advance from domain and task estimators, allowing the model to be pre-adjusted to accommodate new domains while preserving knowledge of previous domains through the Bayesian framework.
Solution Approach 2:
The patent implements parameter changes by modulating the initial parameters of the task execution model based on domain and task characteristics. The Bayesian neural network dynamically adjusts parameters θ and ψ according to the specific domain and task at hand, enabling adaptive learning across multiple domains without complete retraining, thus preventing catastrophic forgetting while maintaining learning efficiency.
2Quantity of substance
If few-shot learning is used to learn with small data amounts, then data efficiency is improved, but the model lacks robustness across different domains
Solution Approach 1:
The patent applies universality by creating a unified Bayesian neural network framework that handles both few-shot learning and multi-domain adaptation simultaneously. The task execution model with parameters θ and ψ serves multiple functions: learning from limited support data (few-shot capability) and adapting to different domains (generalization capability) through a single integrated architecture rather than separate specialized models.
Solution Approach 2:
The patent introduces domain and task estimators as intermediary components that bridge the gap between limited data and robust domain generalization. These estimators acquire domain and task information from the input data and use it to modulate the model parameters, serving as intermediaries that enable the few-shot learned model to adapt to different domains without requiring extensive domain-specific data.
3Loss of information
If continuous learning technologies are used to alleviate forgetting, then past domain retention is improved, but the system complexity increases
Solution Approach 1:
The patent merges multiple continuous learning approaches (normalization-based, rehearsal-based, and dynamic network structure-based methods) into a single Bayesian neural network framework. By combining these techniques into one unified system with parameters θ and ψ that are jointly optimized, the patent reduces overall system complexity compared to implementing separate systems for each continuous learning approach.
Data Source
AI summary
Provided are a method and apparatus for online Bayesian few-shot learning. The present invention provides a method and apparatus for online Bayesian few-shot learning in which multi-domain-based online learning and few-shot learning are integrated when domains of tasks having data are sequentially given.