Robustness of SSF (Scale-and-Shift Features) tuning in complex domains with divergent data distributions

Validate whether the SSF scale-and-shift representation tuning approach produces robust tunable parameters in complex domains whose training data distributions significantly diverge from those used during pretraining.

Background

SSF introduces scale and shift parameters to modulate pretrained features, reporting strong results across several vision backbones and datasets.

The authors state that further verification is required in complex domains with substantial distributional differences from pretraining data.

References

However, obtaining tunable parameters by scaling and shifting original parameters needs further verification in more complex domains whose training data might significantly vary from the ones used in pretraining.

— Towards Incremental Learning in Large Language Models: A Critical Review (2404.18311 - Jovanovic et al., 28 Apr 2024) in Section 2.3 (Parameter-Efficient Learning) – SSF