The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Encoder Based Lifelong Learning
Open accessAbstract
This paper introduces a new lifelong learning solution where a single model is trained for a sequence of tasks. The main challenge that vision systems face in this context is catastrophic forgetting: as they tend to adapt to the most recently seen task, they lose performance on the tasks that were learned previously. Our method aims at preserving the knowledge of the previous tasks while learning a new one by using autoencoders. For each task, an under-complete autoencoder is learned, capturing the features that are crucial for its achievement. When a new task is presented to the system, we prevent the reconstructions of the features with these autoencoders from changing, which has the effect of preserving the information on which the previous tasks are mainly relying. At the same time, the features are given space to adjust to the most recent environment as only their projection into a low dimension submanifold is controlled. The proposed system is evaluated on image classification tasks and shows a reduction of forgetting over the state-ofthe-art.
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“LwF [17] suffers from a buildup of errors when facing a long sequence while EBLL [28] reduces slightly this effect.”
From Memory Aware Synapses: Learning What (not) to Forget (2018) · cites this paper as [28]
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“It focuses on image classification and consists of a sequence of disjoint tasks , which are learned one after the other. Tasks may correspond to different datasets, or different splits of a dataset, without overlap in category labels.”
From Memory Aware Synapses: Learning What (not) to Forget (2018) · cites this paper as [28]
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“Data-based approaches [1,17,28,34] use data from the new task to approximate the performance of the previous tasks. This works best if the data distribution mismatch between tasks is limited.”
From Memory Aware Synapses: Learning What (not) to Forget (2018) · cites this paper as [28]
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Citations
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Metadata
- Authors
- Rannen, A., Aljundi, R., Blaschko, M. B., & Tuytelaars, T.
- Affiliations
- IMEC (BE)
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 1329-1337
- OpenAlex
- W2605911906
- Cited by
- 324
- References
- 23
- Citations by year
- 2026: 10 · 2025: 23 · 2024: 58 · 2023: 53 · 2022: 47 · 2021: 46 · 2020: 41 · 2019: 23 · 2018: 22 · 2017: 1
- FWCI
- 10.85
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Lirias (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- Fonds Wetenschappelijk Onderzoek
- KU Leuven (FP7-MC-CIG 334380)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- lifelong learning, catastrophic forgetting, autoencoder, feature preservation, image classification, vision system
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