mlsanityData to 7 Oct 2026

Unsupervised Continual Learning via Self-Adaptive Deep Clustering Approach

arXiv 2106.14563 ↗First posted 28 Jun 2021cs.LG cs.AIcited by 19

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Abstract (arXiv)

Unsupervised continual learning remains a relatively uncharted territory in the existing literature because the vast majority of existing works call for unlimited access of ground truth incurring expensive labelling cost. Another issue lies in the problem of task boundaries and task IDs which must be known for model's updates or model's predictions hindering feasibility for real-time deployment. Knowledge Retention in Self-Adaptive Deep Continual Learner, (KIERA), is proposed in this paper. KIERA is developed from the notion of flexible deep clustering approach possessing an elastic network structure to cope with changing environments in the timely manner. The centroid-based experience replay is put forward to overcome the catastrophic forgetting problem. KIERA does not exploit any labelled samples for model updates while featuring a task-agnostic merit. The advantage of KIERA has been numerically validated in popular continual learning problems where it shows highly competitive performance compared to state-of-the art approaches. Our implementation is available in textiturlhttps://github.com/ContinualAL/KIERA.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 17 of its 28 references that are in the map. First posted on arXiv 28 Jun 2021.

Builds on

17 of 28 references.

References · 17

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  2. 2016Overcoming catastrophic forgetting in neural networkscited by 4,718
  3. 2016Learning without Forgettingcited by 2,465
  4. 2016iCaRL: Incremental Classifier and Representation Learningcited by 2,313
  5. 2017Gradient Episodic Memory for Continual Learningcited by 1,871
  6. 2016Progressive Neural Networkscited by 1,332
  7. 2018Continual Lifelong Learning with Neural Networks: A Reviewcited by 1,313
  8. 2017Continual Learning with Deep Generative Replaycited by 1,152
  9. 2017Memory Aware Synapses: Learning what (not) to forgetcited by 1,094
  10. 2018Efficient Lifelong Learning with A-GEMcited by 1,044
  11. 2017Lifelong Learning with Dynamically Expandable Networkscited by 689
  12. 2018Progress & Compress: A scalable framework for continual learningcited by 516
5 more
  1. 2019Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgettingcited by 302
  2. 2016Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clusteringcited by 237
  3. 2019Overcoming Catastrophic Forgetting by Neuron-level Plasticity Controlcited by 36
  4. 2021Continual Learning via Inter-Task Synaptic Mappingcited by 14
  5. 2019Automatic Construction of Multi-layer Perceptron Network from Streaming Examplescited by 11

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 4; 2024: 7; 2025: 2; 2026: 2.

19 papers.

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  1. 2022The CLEAR Benchmark: Continual LEArning on Real-World Imagerycited by 92
  2. 2022Reinforced Continual Learning for Graphscited by 24
  3. 2023Continual Learning in Medical Image Analysis: A Comprehensive Review of Recent Advancements and Future Prospectscited by 22
  4. 2022SCALE: Online Self-Supervised Lifelong Learning without Prior Knowledgecited by 16
  5. 2023Assessor-Guided Learning for Continual Environmentscited by 7
  6. 2022Scalable Adversarial Online Continual Learningcited by 6
  7. 2023Few-Shot Continual Learning via Flat-to-Wide Approachescited by 4
  8. 2024Few-Shot Class Incremental Learning via Robust Transformer Approachcited by 4
  9. 2024U-TELL: Unsupervised Task Expert Lifelong Learningcited by 2
  10. 2024Cross-Domain Continual Learning via CLAMPcited by 1
  11. 2024Forward-Backward Knowledge Distillation for Continual Clusteringcited by 1
  12. 2024Unsupervised Few-Shot Continual Learning for Remote Sensing Image Scene Classificationcited by 1
7 more
  1. 2025Continual Knowledge Consolidation LORA for Domain Incremental Learningcited by 1
  2. 2023Spatiotemporal Event Graphs for Dynamic Scene Understandingcited by 0
  3. 2024Sup3r: A Semi-Supervised Algorithm for increasing Sparsity, Stability, and Separability in Hierarchy Of Time-Surfaces architecturescited by 0
  4. 2024Caption, Create, Continue: Continual Learning with Pre-trained Generative Vision-Language Modelscited by 0
  5. 2025Source-Free Cross-Domain Continual Learningcited by 0
  6. 2026Few-Shot Domain Incremental Learning via Continual Vision-Language Consolidationcited by 0
  7. 2026Lifelong Representations: A Survey on Continual Self-Supervised Learning for Vision Modelscited by 0