Adaptive Deep Models for Incremental Learning: Considering Capacity Scalability and Sustainability
Open accessAbstract
Recent years have witnessed growing interests in developing deep models for incremental learning. However, existing approaches often utilize the fixed structure and online backpropagation for deep model optimization, which is difficult to be implemented for incremental data scenarios. Indeed, for streaming data, there are two main challenges for building deep incremental models. First, there is a requirement to develop deep incremental models with Capacity Scalability. In other words, the entire training data are not available before learning the task. It is a challenge to make the deep model structure scaling with streaming data for flexible model evolution and faster convergence. Second, since the stream data distribution usually changes in nature (concept drift), there is a constraint for Capacity Sustainability. That is, how to update the model while preserving previous knowledge for overcoming the catastrophic forgetting. To this end, in this paper, we develop an incremental adaptive deep model (IADM) for dealing with the above two capacity challenges in real-world incremental data scenarios. Specifically, IADM provides an extra attention model for the hidden layers, which aims to learn deep models with adaptive depth from streaming data and enables capacity scalability. Also, we address capacity sustainability by exploiting the attention based fisher information matrix, which can prevent the forgetting in consequence. Finally, we conduct extensive experiments on real-world data and show that IADM outperforms the state-of-the-art methods with a substantial margin. Moreover, we show that IADM has better capacity scalability and sustainability in incremental learning scenarios.
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The experimental protocol of some methods is closer to instance incremental scenario, and they obtained relatively good results; this shows instance incremental scenario is less difficult than class-incremental scenario.
“The experimental protocol of some methods is closer to the instance incremental scenario, and all have obtained relatively good experimental results [15,33]. It shows that the implementation of an instance incremental scenario is less difficult than the class-incremental scenario.”
From An Appraisal of Incremental Learning Methods (2020) · cites this paper as [33]
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- Authors
- Yang, Y., Zhou, D.-W., Zhan, D.-C., Xiong, H., & Jiang, Y.
- Affiliations
- Nanjing University (CN)
- Rutgers, The State University of New Jersey (US)
- Venue
- Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
- Publisher
- ACM
- Published
- 2019-07-25
- Pages
- 74-82
- OpenAlex
- W2949071326
- Cited by
- 83
- References
- 40
- Citations by year
- 2026: 5 · 2025: 13 · 2024: 24 · 2023: 12 · 2022: 6 · 2021: 16 · 2020: 6 · 2019: 1
- FWCI
- 2.77
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
- Funders
- NSF (IIS-1814510)
- NSFC (61773198 61632004)
- National Key R$&$D Program of China (2018YFB1004300)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Data Stream Mining Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
- Keywords
- incremental learning, catastrophic forgetting, concept drift, data streams, attention mechanism, Fisher information matrix, knowledge preservation, online learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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