DART: Domain-Adversarial Residual-Transfer Networks for Unsupervised Cross-Domain Image Classification
arXiv:1812.11478
Abstract
The accuracy of deep learning (e.g., convolutional neural networks) for an image classification task critically relies on the amount of labeled training data. Aiming to solve an image classification task on a new domain that lacks labeled data but gains access to cheaply available unlabeled data, unsupervised domain adaptation is a promising technique to boost the performance without incurring extra labeling cost, by assuming images from different domains share some invariant characteristics. In this paper, we propose a new unsupervised domain adaptation method named Domain-Adversarial Residual-Transfer (DART) learning of Deep Neural Networks to tackle cross-domain image classification tasks. In contrast to the existing unsupervised domain adaption approaches, the proposed DART not only learns domain-invariant features via adversarial training, but also achieves robust domain-adaptive classification via a residual-transfer strategy, all in an end-to-end training framework. We evaluate the performance of the proposed method for cross-domain image classification tasks on several well-known benchmark data sets, in which our method clearly outperforms the state-of-the-art approaches.
References in corpus (11)
- DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
- Learning Transferable Features with Deep Adaptation Networks
- Deep Domain Confusion: Maximizing for Domain Invariance
- Deep Visual Domain Adaptation: A Survey
- Conditional Adversarial Domain Adaptation
- Unsupervised Domain Adaptation with Residual Transfer Networks
- Domain Separation Networks
- Unsupervised Image-to-Image Translation Networks
- Domain Adaptation: Learning Bounds and Algorithms
- Adversarial Discriminative Domain Adaptation
- Unsupervised Cross-Domain Image Generation