Learning a Generative Inverse to Adapt the Forward: Source-Free Domain Adaptation for Sequence Regression

Source-Free Domain Adaptation (SFDA) has emerged as a pragmatic paradigm for adapting models to practical scenarios without access to source data. Most existing SFDA methods focus on image classification and rely on entropy minimization or pseudo-labeling under the cluster assumption. Sequence regression instead has a continuous output space and does not provide discrete category codes. Simply extending classification-style methods to regression often fails to yield satisfactory results and may even lead to negative transfer. To address this issue, we propose Generative Inverse Regression Adaptation (GIRA), a general-purpose SFDA framework designed for sequence regression. Specifically, GIRA defines generalized classes over the continuous output space and uses a conditional generative model to learn the inverse mapping of the pre-trained model, generating associated target-style inputs. These resulting samples provide adaptation signals for updating the regression model on the target domain. Furthermore, uncertainty threshold filtering and feature scale constraints are integrated to mitigate negative transfer and ensure training stability. Across nine transfer scenarios in three tasks, GIRA reduces MAE by 20.87% on average relative to zero-shot and achieves the highest MAE improvement among the compared methods in most tasks.