Deep Adversarial Domain Adaptation Based on Multi-layer Joint Kernelized Distance

10/09/2020
by   Sitong Mao, et al.
0

Domain adaptation refers to the learning scenario that a model learned from the source data is applied on the target data which have the same categories but different distribution. While it has been widely applied, the distribution discrepancy between source data and target data can substantially affect the adaptation performance. The problem has been recently addressed by employing adversarial learning and distinctive adaptation performance has been reported. In this paper, a deep adversarial domain adaptation model based on a multi-layer joint kernelized distance metric is proposed. By utilizing the abstract features extracted from deep networks, the multi-layer joint kernelized distance (MJKD) between the jth target data predicted as the mth category and all the source data of the m'th category is computed. Base on MJKD, a class-balanced selection strategy is utilized in each category to select target data that are most likely to be classified correctly and treat them as labeled data using their pseudo labels. Then an adversarial architecture is used to draw the newly generated labeled training data and the remaining target data close to each other. In this way, the target data itself provide valuable information to enhance the domain adaptation. An analysis of the proposed method is also given and the experimental results demonstrate that the proposed method can achieve a better performance than a number of state-of-the-art methods.

READ FULL TEXT

page 1

page 6

research
03/02/2018

Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift

Unsupervised domain adaptation (UDA) conventionally assumes labeled sour...
research
09/05/2019

Multi-layer Domain Adaptation for Deep Convolutional Networks

Despite their success in many computer vision tasks, convolutional netwo...
research
11/04/2020

Mixed Set Domain Adaptation

In the settings of conventional domain adaptation, categories of the sou...
research
11/04/2020

Against Adversarial Learning: Naturally Distinguish Known and Unknown in Open Set Domain Adaptation

Open set domain adaptation refers to the scenario that the target domain...
research
09/21/2020

Graph Based Multi-layer K-means++ (G-MLKM) for Sensory Pattern Analysis in Constrained Spaces

In this paper, we focus on developing a novel unsupervised machine learn...
research
04/21/2019

MiniMax Entropy Network: Learning Category-Invariant Features for Domain Adaptation

How to effectively learn from unlabeled data from the target domain is c...
research
06/11/2020

Recurrent Neural Networks for Handover Management in Next-Generation Self-Organized Networks

In this paper, we discuss a handover management scheme for Next Generati...

Please sign up or login with your details

Forgot password? Click here to reset