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A Memory-Free Evolving Bipolar Neural Network for Efficient Multi-Label Stream Learning

Mishra, S and Sundaram, S (2023) A Memory-Free Evolving Bipolar Neural Network for Efficient Multi-Label Stream Learning. In: UNSPECIFIED.

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Official URL: https://doi.org/10.1109/ICASSP49357.2023.10095002


Many fields, like document tagging, video labeling, and medical analysis, require associating the samples with multiple non-exclusive labels, driving the research in multi-label learning. Unlike several multi-label learning setups, practical applications are challenging because they need learning from a stream of samples and labels. This work proposes an Evolving Bipolar Network architecture called EBN-MSL consisting of two parallel layers trained in a maximum margin framework to learn efficiently in a continual multi-label learning scenario without utilizing any samples stored from previous tasks. This work considers two learning setups, one for separately learning each task (SEA�) and another for jointly learning subsequent tasks (SEA). Experiments on benchmark multi-label learning datasets establish the superior learning capability of EBN-MSL in the presence of samples having all positive or negative labels. Results indicate that EBN-MSL (both SEA and SEA� setups) significantly outperforms the current state-of-the-art architecture-based continual multi-label learning algorithm. © 2023 IEEE.

Item Type: Conference Paper
Publication: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Publisher: Institute of Electrical and Electronics Engineers Inc.
Additional Information: The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Department/Centre: Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited: 04 Mar 2024 07:53
Last Modified: 04 Mar 2024 07:53
URI: https://eprints.iisc.ac.in/id/eprint/84279

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