Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations
Journal:
arXiv
Published Date:
Jan 2, 2025
Abstract
Multi-source Domain Adaptation (MDA) aims to transfer knowledge from multiple
labeled source domains to an unlabeled target domain. Nevertheless, traditional
methods primarily focus on achieving inter-domain alignment through
sample-level constraints, such as Maximum Mean Discrepancy (MMD), neglecting
three pivotal aspects: 1) the potential of data augmentation, 2) the
significance of intra-domain alignment, and 3) the design of cluster-level
constraints. In this paper, we introduce a novel hardness-driven strategy for
MDA tasks, named "A3MDA" , which collectively considers these three aspects
through Adaptive hardness quantification and utilization in both data
Augmentation and domain Alignment.To achieve this, "A3MDA" progressively
proposes three Adaptive Hardness Measurements (AHM), i.e., Basic, Smooth, and
Comparative AHMs, each incorporating distinct mechanisms for diverse scenarios.
Specifically, Basic AHM aims to gauge the instantaneous hardness for each
source/target sample. Then, hardness values measured by Smooth AHM will
adaptively adjust the intensity level of strong data augmentation to maintain
compatibility with the model's generalization capacity.In contrast, Comparative
AHM is designed to facilitate cluster-level constraints. By leveraging hardness
values as sample-specific weights, the traditional MMD is enhanced into a
weighted-clustered variant, strengthening the robustness and precision of
inter-domain alignment. As for the often-neglected intra-domain alignment, we
adaptively construct a pseudo-contrastive matrix by selecting harder samples
based on the hardness rankings, enhancing the quality of pseudo-labels, and
shaping a well-clustered target feature space. Experiments on multiple MDA
benchmarks show that " A3MDA " outperforms other methods.
