Online and replay-based: research map

582 accepted papers on Online and replay-based in Continual learning, from ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026), grouped into 2 clusters and 8 approaches. The busiest year so far is 2026.

Within Continual learning, its share shrank from 46.4% in 2023–24 to 34.2% in 2025–26 (163 → 197 papers at ICML, NeurIPS, CVPR and AAAI, the venues with data for all four years).

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Approaches and key papers

weight · transfer · growing · 454 papers

Approaches in this cluster:

Theoretical analysis of forgetting (141 papers)
Studies why and when networks forget, using linear models, teacher-student setups and complexity results.
Unified and unsupervised continual frameworks (120 papers)
Proposes general frameworks, benchmarks and architecture-growing methods for task-agnostic and unsupervised continual learning.
Gradient projection and plasticity preservation (80 papers)
Constrains updates to subspaces or preserves network trainability to limit forgetting.
Dynamic expansion and isolation (70 papers)
Grows or partitions networks and generators to keep task knowledge separate.
Plasticity-preserving continual reinforcement learning (43 papers)
Keeps policies adaptable using architectural design, policy subspaces and composition across tasks.

Most cited and most cited since 2024:

PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning (CVPR 2018 · 1,240 citations)
Continual Learning Through Synaptic Intelligence (ICML 2017 · 1,007 citations)
Towards Continual Knowledge Graph Embedding via Incremental Distillation (AAAI 2024 · 42 citations)
Towards Efficient Replay in Federated Incremental Learning (CVPR 2024 · 34 citations)

online continual · replay · experience · 128 papers