IDAL: Improved Domain Adaptive Learning for Natural Images Dataset

TL;DR

This paper introduces IDAL, a novel unsupervised domain adaptation method for natural images that improves domain alignment using a specialized neural architecture and a tailored loss function, leading to better performance and faster training.

Contribution

IDAL combines ResNet and FPN architectures with a novel loss function to effectively address scale, noise, and style shifts in natural images for domain adaptation.

Findings

Outperforms state-of-the-art on Office-Home, Office-31, VisDA-2017 datasets.

Achieves comparable results on DomainNet dataset.

Speeds up training convergence and improves robustness.

Abstract

We present a novel approach for unsupervised domain adaptation (UDA) for natural images. A commonly-used objective for UDA schemes is to enhance domain alignment in representation space even if there is a domain shift in the input space. Existing adversarial domain adaptation methods may not effectively align different domains of multimodal distributions associated with classification problems. Our approach has two main features. Firstly, its neural architecture uses the deep structure of ResNet and the effective separation of scales of feature pyramidal network (FPN) to work with both content and style features. Secondly, it uses a combination of a novel loss function and judiciously selected existing loss functions to train the network architecture. This tailored combination is designed to address challenges inherent to natural images, such as scale, noise, and style shifts, that occur on top of a multi-modal (multi-class) distribution. The combined loss function not only enhances model accuracy and robustness on the target domain but also speeds up training convergence. Our proposed UDA scheme generalizes better than state-of-the-art for CNN-based methods on Office-Home, Office-31, and VisDA-2017 datasets and comaparable for DomainNet dataset.