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
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The relative rankings of continual learning methods are not preserved across different fine-tuning regimes defined by trainable parameter depth.
citing papers explorer
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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.
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Fine-Tuning Regimes Define Distinct Continual Learning Problems
The relative rankings of continual learning methods are not preserved across different fine-tuning regimes defined by trainable parameter depth.