Modified Distribution Alignment for Domain Adaptation with Pre-trained Inception ResNet
arXiv:1904.02322
Abstract
Deep neural networks have been widely used in computer vision. There are several well trained deep neural networks for the ImageNet classification challenge, which has played a significant role in image recognition. However, little work has explored pre-trained neural networks for image recognition in domain adaption. In this paper, we are the first to extract better-represented features from a pre-trained Inception ResNet model for domain adaptation. We then present a modified distribution alignment method for classification using the extracted features. We test our model using three benchmark datasets (Office+Caltech-10, Office-31, and Office-Home). Extensive experiments demonstrate significant improvements (4.8%, 5.5%, and 10%) in classification accuracy over the state-of-the-art.
References in corpus (7)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
- Deep Domain Confusion: Maximizing for Domain Invariance
- Conditional Adversarial Domain Adaptation
- Unsupervised Domain Adaptation with Residual Transfer Networks
- Domain Separation Networks
- Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation
Cited by in corpus (5)
- Adversarial Consistent Learning on Partial Domain Adaptation of PlantCLEF 2020 Challenge
- Impact of ImageNet Model Selection on Domain Adaptation
- Efficient Pre-trained Features and Recurrent Pseudo-Labeling in Unsupervised Domain Adaptation
- Deep Least Squares Alignment for Unsupervised Domain Adaptation
- Deep Spherical Manifold Gaussian Kernel for Unsupervised Domain Adaptation