This paper introduces the idSprites benchmark and a disentangled learning framework designed to address the limitations of current continual learning

This paper compares a novel continual learning method's performance in online learning versus batch training scenarios.

This paper evaluates our model's performance in one-shot learning and open-set classification tasks.

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

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

Author: Dibin Mathew

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This paper evaluates standard regularization methods, including Learning without Forgetting (LwF) and Synaptic Intelligence (SI).

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This article evaluates various continual learning methods, including a novel disentangled learning framework, using the idSprites benchmark.

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

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This paper presents Infinite dSprites, a novel framework for creating long continual learning benchmarks, alongside a conceptual disentangled learning approach.

This article critiques current class-incremental continual learning methods, highlighting their limitations and proposing solutions.

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A novel continual learning approach overcomes catastrophic forgetting by separating class-specific memorization from class-agnostic generalization.

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Planning too far ahead can backfire. It can slow your time to market, reduce flexibility, and increase burn rate.