Papers › Gradient Projection Memory for Continual Learning

Gradient Projection Memory for Continual Learning

17 Mar 2021ICLR 2021 1arXiv:2103.09762archive 2025-07-28

Gobinda Saha, Isha Garg, Kaushik Roy

The ability to learn continually without forgetting the past tasks is a desired attribute for artificial learning systems. Existing approaches to enable such learning in artificial neural networks usually rely on network growth, importance based weight update or replay of old data from the memory. In contrast, we propose a novel approach where a neural network learns new tasks by taking gradient steps in the orthogonal direction to the gradient subspaces deemed important for the past tasks. We find the bases of these subspaces by analyzing network representations (activations) after learning each task with Singular Value Decomposition (SVD) in a single shot manner and store them in the memory as Gradient Projection Memory (GPM). With qualitative and quantitative analyses, we show that such orthogonal gradient descent induces minimum to no interference with the past tasks, thereby mitigates forgetting. We evaluate our algorithm on diverse image classification datasets with short and long sequences of tasks and report better or on-par performance compared to the state-of-the-art approaches.

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1ran · honoured contract
2ran · our draft was wrong
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compute_conv_output_size sahagobinda/GPM/main_cifar100.py official repository ran · honoured contract MIT (permissive) · e71c2a2bc2fc8909 · report
get_model sahagobinda/GPM/main_cifar100.py official repository ran · our draft was wrong MIT (permissive) · ebd151f2b9fc07af · report
test sahagobinda/GPM/main_cifar100.py official repository ran · our draft was wrong MIT (permissive) · 52f190e75c2b2866 · report
update_GPM sahagobinda/GPM/main_cifar100.py official repository ran · fixture could not drive it MIT (permissive) · 6e3c5f8afe521af7 · report

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AttributeContinual LearningImage Classificationimage-classification

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