Looking Through the Past: Better Knowledge Retention for Generative Replay in Continual Learning
In this work, we improve the generative replay in a continual learning setting to perform well on challenging scenarios. Because of the growing complexity of continual learning tasks, it is becoming more popular, to apply the generative replay technique in the feature space instead of image space. N...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10474374/ |
| _version_ | 1797231204278206464 |
|---|---|
| author | Valeriya Khan Sebastian Cygert Kamil Deja Tomasz Trzcinski Bartlomiej Twardowski |
| author_facet | Valeriya Khan Sebastian Cygert Kamil Deja Tomasz Trzcinski Bartlomiej Twardowski |
| author_sort | Valeriya Khan |
| collection | DOAJ |
| description | In this work, we improve the generative replay in a continual learning setting to perform well on challenging scenarios. Because of the growing complexity of continual learning tasks, it is becoming more popular, to apply the generative replay technique in the feature space instead of image space. Nevertheless, such an approach does not come without limitations. In particular, we notice the degradation of the continually trained model’s performance could be attributed to the fact that the generated features are far from the original ones when mapped to the latent space. Therefore, we propose three modifications that mitigate these issues. More specifically, we incorporate the distillation in latent space between the current and previous models to reduce feature drift. Additionally, a latent matching for the reconstruction and original data is proposed to improve generated features alignment. Further, based on the observation that the reconstructions are better for preserving knowledge, we add the cycling of generations through the previously trained model to make them closer to the original data. Our method outperforms other generative replay methods in various scenarios. Code available at <uri>https://github.com/valeriya-khan/looking-through-the-past</uri>. |
| first_indexed | 2024-04-24T15:40:40Z |
| format | Article |
| id | doaj.art-61daa4fe4f7e4d849ef9c7961da36e3d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T15:40:40Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-61daa4fe4f7e4d849ef9c7961da36e3d2024-04-01T23:00:43ZengIEEEIEEE Access2169-35362024-01-0112453094531710.1109/ACCESS.2024.337914810474374Looking Through the Past: Better Knowledge Retention for Generative Replay in Continual LearningValeriya Khan0https://orcid.org/0009-0003-3505-3831Sebastian Cygert1https://orcid.org/0000-0002-4763-8381Kamil Deja2https://orcid.org/0000-0003-1156-5544Tomasz Trzcinski3https://orcid.org/0000-0002-1486-8906Bartlomiej Twardowski4https://orcid.org/0000-0003-2117-8679IDEAS NCBR, Warsaw, PolandIDEAS NCBR, Warsaw, PolandIDEAS NCBR, Warsaw, PolandIDEAS NCBR, Warsaw, PolandIDEAS NCBR, Warsaw, PolandIn this work, we improve the generative replay in a continual learning setting to perform well on challenging scenarios. Because of the growing complexity of continual learning tasks, it is becoming more popular, to apply the generative replay technique in the feature space instead of image space. Nevertheless, such an approach does not come without limitations. In particular, we notice the degradation of the continually trained model’s performance could be attributed to the fact that the generated features are far from the original ones when mapped to the latent space. Therefore, we propose three modifications that mitigate these issues. More specifically, we incorporate the distillation in latent space between the current and previous models to reduce feature drift. Additionally, a latent matching for the reconstruction and original data is proposed to improve generated features alignment. Further, based on the observation that the reconstructions are better for preserving knowledge, we add the cycling of generations through the previously trained model to make them closer to the original data. Our method outperforms other generative replay methods in various scenarios. Code available at <uri>https://github.com/valeriya-khan/looking-through-the-past</uri>.https://ieeexplore.ieee.org/document/10474374/Continual learninggenerative replaymachine learning |
| spellingShingle | Valeriya Khan Sebastian Cygert Kamil Deja Tomasz Trzcinski Bartlomiej Twardowski Looking Through the Past: Better Knowledge Retention for Generative Replay in Continual Learning IEEE Access Continual learning generative replay machine learning |
| title | Looking Through the Past: Better Knowledge Retention for Generative Replay in Continual Learning |
| title_full | Looking Through the Past: Better Knowledge Retention for Generative Replay in Continual Learning |
| title_fullStr | Looking Through the Past: Better Knowledge Retention for Generative Replay in Continual Learning |
| title_full_unstemmed | Looking Through the Past: Better Knowledge Retention for Generative Replay in Continual Learning |
| title_short | Looking Through the Past: Better Knowledge Retention for Generative Replay in Continual Learning |
| title_sort | looking through the past better knowledge retention for generative replay in continual learning |
| topic | Continual learning generative replay machine learning |
| url | https://ieeexplore.ieee.org/document/10474374/ |
| work_keys_str_mv | AT valeriyakhan lookingthroughthepastbetterknowledgeretentionforgenerativereplayincontinuallearning AT sebastiancygert lookingthroughthepastbetterknowledgeretentionforgenerativereplayincontinuallearning AT kamildeja lookingthroughthepastbetterknowledgeretentionforgenerativereplayincontinuallearning AT tomasztrzcinski lookingthroughthepastbetterknowledgeretentionforgenerativereplayincontinuallearning AT bartlomiejtwardowski lookingthroughthepastbetterknowledgeretentionforgenerativereplayincontinuallearning |