  Parameter-efficient transfer learning (PETL) has emerged as a flourishing
research field for adapting large pre-trained models to downstream tasks,
greatly reducing trainable parameters while grappling with memory challenges
during fine-tuning. To address it, memory-efficient series (METL) avoid
backpropagating gradients through the large backbone. However, they compromise
by exclusively relying on frozen intermediate outputs and limiting the
exhaustive exploration of prior knowledge from pre-trained models. Moreover,
the dependency and redundancy between cross-layer features are frequently
overlooked, thereby submerging more discriminative representations and causing
an inherent performance gap (vs. conventional PETL methods). Hence, we propose
an innovative METL strategy called SHERL for resource-limited scenarios to
decouple the entire adaptation into two successive and complementary processes.
In the early route, intermediate outputs are consolidated via an
anti-redundancy operation, enhancing their compatibility for subsequent
interactions; thereby in the late route, utilizing minimal late pre-trained
layers could alleviate the peak demand on memory overhead and regulate these
fairly flexible features into more adaptive and powerful representations for
new domains. Extensive ablations on vision-and-language and language-only tasks
show that SHERL combines the strengths of both parameter and memory-efficient
techniques, performing on-par or better across diverse architectures with lower
memory during fine-tuning. Our code is publicly available at:
https://github.com/Paranioar/SHERL.
