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.