Deep Bayesian Unsupervised Lifelong Learning.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Bayesian Inference

Background:

• Lifelong Learning (LL) enables continuous adaptation with new data.
• Supervised Lifelong Learning (SLL) typically requires labeled data streams.
• Unsupervised Lifelong Learning (ULL) faces challenges with evolving data distributions and unknown labels in unlabeled data.

Purpose of the Study:

• To address challenges in Unsupervised Lifelong Learning (ULL) with evolving unlabeled data.
• To develop a Bayesian framework for ULL that incorporates past knowledge and updates beliefs sequentially.
• To introduce a novel algorithm for ULL that discovers new clusters without catastrophic forgetting.

Main Methods:

• Developed a fully Bayesian inference framework for ULL.
• Introduced a Deep Bayesian Unsupervised Lifelong Learning (DBULL) algorithm.
• Implemented a knowledge preservation mechanism using sufficient statistics of latent representations.
• Designed an automatic cluster discovery and redundancy removal strategy inspired by Nonparametric Bayesian statistics.

Main Results:

• The DBULL algorithm progressively discovers new clusters using unlabeled data.
• The approach effectively retains previously learned knowledge without forgetting.
• Learned latent representations from evolving data distributions.
• Demonstrated effectiveness on image and text benchmark datasets in both lifelong and batch settings.

Conclusions:

• The proposed DBULL algorithm offers a robust solution for Unsupervised Lifelong Learning.
• The Bayesian framework facilitates sequential updates and knowledge retention in dynamic environments.
• The method shows promise for real-world applications requiring continuous learning from unlabeled data.