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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

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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.

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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.

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Why Equivariance Outperforms Invariant Learning in Continual Learning Tasks

27 Aug 2024

This paper contrasts equivariant and invariant representation learning for continual learning.

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Continual learning and Benchmarking continual learning

27 Aug 2024

This article reviews key techniques in continual learning, including parameter isolation, regularization, and replay methods.

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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).

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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.

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How Disentangled Learning Tackles Catastrophic Forgetting

27 Aug 2024

Explore how disentangled learning separates generalization from memorization in continual learning.

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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.