Autoencoders Covering Space as a Life-Long Classifier
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00332666" target="_blank" >RIV/68407700:21230/19:00332666 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-030-19642-4_27" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-19642-4_27</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-19642-4_27" target="_blank" >10.1007/978-3-030-19642-4_27</a>
Alternative languages
Result language
angličtina
Original language name
Autoencoders Covering Space as a Life-Long Classifier
Original language description
A life-long classifier that learns incrementally has many challenges such as concept drift, when the class changes in time, and catastrophic forgetting when the earlier learned knowledge is lost. Many successful connectionist solutions are based on an idea that new data are learned only in a part of a network that is relevant to the new data. We leverage this idea and propose a novel method for learning an ensemble of specialized autoencoders. We interpret autoencoders as manifolds that can be trained to contain or exclude given points from the input space. This manifold manipulation allows us to implement a classifier that can suppress catastrophic forgetting and adapt to concept drift. The proposed algorithm is evaluated on an incremental version of the XOR problem and on an incremental version of the MNIST classification where we achieved 0.9 accuracy which is a significant improvement over the previously published results
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA18-18858S" target="_blank" >GA18-18858S: Robotic Lifelong Learning of Multi-legged Robot Locomotion Control in Autonomous Data Collection Missions</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization
ISBN
978-3-030-19641-7
ISSN
2194-5357
e-ISSN
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Number of pages
11
Pages from-to
271-281
Publisher name
Springer-VDI-Verlag
Place of publication
Düsseldorf
Event location
Barcelona
Event date
Jun 26, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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