Abstract
Traditional transfer learning typically reuses large pre-trained networks by freezing some of their weights and adding task-specific layers. While this approach is computationally efficient, it limits the model's ability to adapt to domain-specific features and can still lead to overfitting with very limited data. To address these limitations, we propose Structured Output Regularization (SOR), a simple yet effective framework that freezes the internal network structures (e.g., convolutional filters) while using a combination of group lasso and $L_1$ penalties. This framework tailors the model to specific data with minimal additional parameters and is easily applicable to various network components, such as convolutional filters or various blocks in neural networks enabling broad applicability for transfer learning tasks. We evaluate SOR on three few shot medical imaging classification tasks and we achieve competitive results using DenseNet121, and EfficientNetB4 bases compared to established benchmarks.
Abstract (translated)
传统的迁移学习通常通过冻结部分预训练网络的权重并添加特定任务层来重用大型预训练网络。虽然这种方法在计算上是高效的，但它限制了模型适应领域特有特征的能力，并且在数据非常有限的情况下仍可能导致过拟合。为了解决这些局限性，我们提出了一种名为结构化输出正则化（SOR）的简单而有效的框架，在该框架中冻结内部网络结构（例如卷积滤波器），同时使用组套索和$L_1$惩罚的组合方法。此框架能够利用最少的额外参数使模型适应特定数据，并且易于应用于各种网络组件，如卷积滤波器或神经网络中的各个块，从而在迁移学习任务中具有广泛的适用性。我们在三个少量样本医学图像分类任务上评估了SOR，并使用DenseNet121和EfficientNetB4作为基础模型时取得了与现有基准相当的结果。
URL
https://arxiv.org/abs/2510.08728
