Different valid temporal partitions of the same streaming dataset can produce materially different rankings and performance numbers for continual learning methods.
org/abs/1805.09733
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
CONDITIONAL 2representative citing papers
Relative rankings of online EWC, LwF, SI and GEM are not consistently preserved across five trainable-depth regimes on five datasets and eleven task orders.
citing papers explorer
-
Temporal Taskification in Streaming Continual Learning: A Source of Evaluation Instability
Different valid temporal partitions of the same streaming dataset can produce materially different rankings and performance numbers for continual learning methods.
-
Fine-Tuning Regimes Define Distinct Continual Learning Problems
Relative rankings of online EWC, LwF, SI and GEM are not consistently preserved across five trainable-depth regimes on five datasets and eleven task orders.