Bayesian Continual Learning to Prevent Catastrophic Forgetting
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Solution Overview
Problem
Existing continual-learning techniques suffer from catastrophic forgetting, where performance on past data declines as new information is continuously learned, leading to poor overall performance on non-stationary data streams.
Innovation Solution
A meta continual-learning method using Bayesian updates, employing a meta-learned data distribution learner, Bayes' calculator, and inference engine to maintain performance on both new and old data by calculating likelihood, updating posterior distributions, and sampling latent variables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If continual-learning techniques train models using stochastic gradient descent to learn new data, then the model can continuously update with new information, but catastrophic forgetting occurs and performance on past data continuously declines
Solution Approach 1:
The system performs preliminary actions by maintaining a posterior distribution over latent variables that encodes knowledge from past data before learning new data. This posterior distribution is calculated using Bayes' theorem and serves as a prior for subsequent learning, allowing the model to retain past knowledge while adapting to new information.
Solution Approach 2:
The patent introduces latent variables as intermediaries between observed data and model parameters. These latent variables serve as a buffer that allows the model to incorporate new information while preserving knowledge about past data through the posterior distribution, thereby preventing catastrophic forgetting.
2Productivity
If new information continuously overwrites previously learned model, then the model can learn new data efficiently, but performance on all continuously inflowing data simultaneously deteriorates
Solution Approach 1:
The system dynamically updates the posterior distribution over latent variables as new data arrives, rather than statically overwriting model parameters. This dynamic updating process allows the model to adapt to new information while maintaining a probabilistic representation of past knowledge, balancing learning efficiency with overall performance.
Solution Approach 2:
The patent changes the parameter representation from fixed model weights to a posterior distribution over latent variables. This parameter change enables the model to capture uncertainty and maintain multiple hypotheses about the data distribution, allowing efficient learning of new data while preserving performance on past data through the evolved posterior distribution.
Data Source
AI summary
A meta continual-learning and inferring method uses an implementation of Bayes' theorem, and includes: calculating a likelihood of learning data for a given latent variable by a data distribution learner; performing a sequential Bayesian update and calculating a final posterior distribution of the latent variable by using prior distribution of the latent variable and the calculated likelihood by a Bayes' calculator; sampling the latent variable from the final posterior distribution; and inferring test output data based on the sampled latent variable and test input data by an inference engine, wherein respective meta parameters of a neural network of the data distribution learner, the prior distribution of the latent variable of the Bayes' calculator, and a neural network of the inference engine are trained by a meta learning.