The ability to learn tasks in a sequential fashion is crucial to the
development of artificial intelligence. Neural networks are not, in general,
capable of this and it has been widely thought that catastrophic forgetting is
an inevitable feature of connectionist models. We show that it is possible to
overcome this limitation and train networks that can maintain expertise on
tasks which they have not experienced for a long time. Our approach remembers
old tasks by selectively slowing down learning on the weights important for
those tasks. We demonstrate our approach is scalable and effective by solving a
set of classification tasks based on the MNIST hand written digit dataset and
by learning several Atari 2600 games sequentially.
Beschreibung
Overcoming catastrophic forgetting in neural networks
%0 Generic
%1 kirkpatrick2016overcoming
%A Kirkpatrick, James
%A Pascanu, Razvan
%A Rabinowitz, Neil
%A Veness, Joel
%A Desjardins, Guillaume
%A Rusu, Andrei A.
%A Milan, Kieran
%A Quan, John
%A Ramalho, Tiago
%A Grabska-Barwinska, Agnieszka
%A Hassabis, Demis
%A Clopath, Claudia
%A Kumaran, Dharshan
%A Hadsell, Raia
%D 2016
%K dfg-antrag-steckbriefe neuralnets
%T Overcoming catastrophic forgetting in neural networks
%U http://arxiv.org/abs/1612.00796
%X The ability to learn tasks in a sequential fashion is crucial to the
development of artificial intelligence. Neural networks are not, in general,
capable of this and it has been widely thought that catastrophic forgetting is
an inevitable feature of connectionist models. We show that it is possible to
overcome this limitation and train networks that can maintain expertise on
tasks which they have not experienced for a long time. Our approach remembers
old tasks by selectively slowing down learning on the weights important for
those tasks. We demonstrate our approach is scalable and effective by solving a
set of classification tasks based on the MNIST hand written digit dataset and
by learning several Atari 2600 games sequentially.
@misc{kirkpatrick2016overcoming,
abstract = {The ability to learn tasks in a sequential fashion is crucial to the
development of artificial intelligence. Neural networks are not, in general,
capable of this and it has been widely thought that catastrophic forgetting is
an inevitable feature of connectionist models. We show that it is possible to
overcome this limitation and train networks that can maintain expertise on
tasks which they have not experienced for a long time. Our approach remembers
old tasks by selectively slowing down learning on the weights important for
those tasks. We demonstrate our approach is scalable and effective by solving a
set of classification tasks based on the MNIST hand written digit dataset and
by learning several Atari 2600 games sequentially.},
added-at = {2021-03-08T13:29:27.000+0100},
author = {Kirkpatrick, James and Pascanu, Razvan and Rabinowitz, Neil and Veness, Joel and Desjardins, Guillaume and Rusu, Andrei A. and Milan, Kieran and Quan, John and Ramalho, Tiago and Grabska-Barwinska, Agnieszka and Hassabis, Demis and Clopath, Claudia and Kumaran, Dharshan and Hadsell, Raia},
biburl = {https://www.bibsonomy.org/bibtex/22ea84f749a616200ad31eef96aa69267/albinzehe},
description = {Overcoming catastrophic forgetting in neural networks},
interhash = {533c2d5e265b0be2495da8d11534b5b0},
intrahash = {2ea84f749a616200ad31eef96aa69267},
keywords = {dfg-antrag-steckbriefe neuralnets},
note = {cite arxiv:1612.00796},
timestamp = {2021-03-08T13:29:27.000+0100},
title = {Overcoming catastrophic forgetting in neural networks},
url = {http://arxiv.org/abs/1612.00796},
year = 2016
}