Domain 11-5

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

5 shown of 5 papers with code (5 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Learning without Forgetting
  29 Jun 2016 12 repositories listed Syntology ran 9 of 15 samples · 6 unverified · 1 pointer-only (licence)
  We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities.
• PLOP: Learning without Forgetting for Continual Semantic Segmentation
  23 Nov 2020 2 repositories listed
  classes predicted by the old model to deal with background shift and avoid catastrophic forgetting of the old classes.
• Incremental Learning Techniques for Semantic Segmentation
  31 Jul 2019 2 repositories listed Syntology ran 0 of 3 samples · 3 unverified
  To tackle this task we propose to distill the knowledge of the previous model to retain the information about previously learned classes, whilst updating the current model to learn the new ones.
• Representation Compensation Networks for Continual Semantic Segmentation
  10 Mar 2022 1 repository listed Syntology ran 0 of 1 samples · 1 unverified
  In this work, we study the continual semantic segmentation problem, where the deep neural networks are required to incorporate new classes continually without catastrophic forgetting.
• Modeling the Background for Incremental Learning in Semantic Segmentation
  3 Feb 2020 1 repository listed Syntology ran 1 of 7 samples · 6 unverified
  Current strategies fail on this task because they do not consider a peculiar aspect of semantic segmentation: since each training step provides annotation only for a subset of all possible classes, pixels of the…

Syntology lines on 4 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.