Existing domain adaptation (DA) methods often involve pre-training on the
source domain and fine-tuning on the target domain. For multi-target domain
adaptation, having a dedicated/separate fine-tuned network for each target
domain, that retain all the pre-trained model parameters, is prohibitively
expensive. To address this limitation, we propose Convolutional Low-Rank
Adaptation (ConvLoRA). ConvLoRA freezes pre-trained model weights, adds
trainable low-rank decomposition matrices to convolutional layers, and
backpropagates the gradient through these matrices thus greatly reducing the
number of trainable parameters. To further boost adaptation, we utilize
Adaptive Batch Normalization (AdaBN) which computes target-specific running
statistics and use it along with ConvLoRA. Our method has fewer trainable
parameters and performs better or on-par with large independent fine-tuned
networks (with less than 0.9% trainable parameters of the total base model)
when tested on the segmentation of Calgary-Campinas dataset containing brain
MRI images. Our approach is simple, yet effective and can be applied to any
deep learning-based architecture which uses convolutional and batch
normalization layers. Code is available at:
this https URL

使用 Convolutional Low-Rank Adaptation（ConvLoRA）和 Adaptive Batch Normalization（AdaBN）提出了一种简单且有效的领域自适应方法，能够减少可训练参数并在测试加拿大卡尔加里和坎皮纳斯数据集中的脑 MRI 图像分割任务中表现良好。