Frequency Composition for Compressed and Domain-Adaptive Neural Networks
Journal:
arXiv
Published Date:
May 27, 2025
Abstract
Modern on-device neural network applications must operate under resource
constraints while adapting to unpredictable domain shifts. However, this
combined challenge-model compression and domain adaptation-remains largely
unaddressed, as prior work has tackled each issue in isolation: compressed
networks prioritize efficiency within a fixed domain, whereas large, capable
models focus on handling domain shifts. In this work, we propose CoDA, a
frequency composition-based framework that unifies compression and domain
adaptation. During training, CoDA employs quantization-aware training (QAT)
with low-frequency components, enabling a compressed model to selectively learn
robust, generalizable features. At test time, it refines the compact model in a
source-free manner (i.e., test-time adaptation, TTA), leveraging the
full-frequency information from incoming data to adapt to target domains while
treating high-frequency components as domain-specific cues. LFC are aligned
with the trained distribution, while HFC unique to the target distribution are
solely utilized for batch normalization. CoDA can be integrated synergistically
into existing QAT and TTA methods. CoDA is evaluated on widely used
domain-shift benchmarks, including CIFAR10-C and ImageNet-C, across various
model architectures. With significant compression, it achieves accuracy
improvements of 7.96%p on CIFAR10-C and 5.37%p on ImageNet-C over the
full-precision TTA baseline.
