How Our Disentangled Learning Framework Tackles Lifelong Learning Challenges
27 Aug 2024
This paper introduces the idSprites benchmark and a disentangled learning framework designed to address the limitations of current continual learning
Batch Training vs. Online Learning
27 Aug 2024
This paper compares a novel continual learning method's performance in online learning versus batch training scenarios.
One-Shot Generalization and Open-Set Classification
27 Aug 2024
This paper evaluates our model's performance in one-shot learning and open-set classification tasks.
Why Equivariance Outperforms Invariant Learning in Continual Learning Tasks
27 Aug 2024
This paper contrasts equivariant and invariant representation learning for continual learning.
Continual learning and Benchmarking continual learning
27 Aug 2024
This article reviews key techniques in continual learning, including parameter isolation, regularization, and replay methods.
How Effective Are Standard Regularization and Replay Methods for Class-Incremental Learning?
27 Aug 2024
This paper evaluates standard regularization methods, including Learning without Forgetting (LwF) and Synaptic Intelligence (SI).
Assessing Generalization and Open-Set Classification in Continual Learning Experiments
27 Aug 2024
This article evaluates various continual learning methods, including a novel disentangled learning framework, using the idSprites benchmark.
How Disentangled Learning Tackles Catastrophic Forgetting
27 Aug 2024
Explore how disentangled learning separates generalization from memorization in continual learning.
Unlocking New Potential in Continual Learning with the Infinite dSprites Framework
27 Aug 2024
This paper presents Infinite dSprites, a novel framework for creating long continual learning benchmarks, alongside a conceptual disentangled learning approach.