Semi Supervised Heterogeneous Domain Adaptation via Disentanglement and Pseudo-Labelling

TL;DR

This paper introduces SHeDD, a neural framework for semi-supervised heterogeneous domain adaptation that disentangles domain-invariant features and uses pseudo-labeling to improve classification across different sensor modalities.

Contribution

The paper proposes SHeDD, a novel end-to-end neural method that effectively handles modality heterogeneity and leverages unlabeled data through disentanglement and augmentation-based regularization.

Findings

SHeDD outperforms baseline methods on remote sensing benchmarks.

Disentanglement improves cross-modality transfer.

Pseudo-labeling enhances target domain classification accuracy.

Abstract

Semi-supervised domain adaptation methods leverage information from a source labelled domain with the goal of generalizing over a scarcely labelled target domain. While this setting already poses challenges due to potential distribution shifts between domains, an even more complex scenario arises when source and target data differs in modality representation (e.g. they are acquired by sensors with different characteristics). For instance, in remote sensing, images may be collected via various acquisition modes (e.g. optical or radar), different spectral characteristics (e.g. RGB or multi-spectral) and spatial resolutions. Such a setting is denoted as Semi-Supervised Heterogeneous Domain Adaptation (SSHDA) and it exhibits an even more severe distribution shift due to modality heterogeneity across domains.To cope with the challenging SSHDA setting, here we introduce SHeDD (Semi-supervised Heterogeneous Domain Adaptation via Disentanglement) an end-to-end neural framework tailored to learning a target domain classifier by leveraging both labelled and unlabelled data from heterogeneous data sources. SHeDD is designed to effectively disentangle domain-invariant representations, relevant for the downstream task, from domain-specific information, that can hinder the cross-modality transfer. Additionally, SHeDD adopts an augmentation-based consistency regularization mechanism that takes advantages of reliable pseudo-labels on the unlabelled target samples to further boost its generalization ability on the target domain. Empirical evaluations on two remote sensing benchmarks, encompassing heterogeneous data in terms of acquisition modes and spectral/spatial resolutions, demonstrate the quality of SHeDD compared to both baseline and state-of-the-art competing approaches. Our code is publicly available here: https://github.com/tanodino/SSHDA/