HoMM: Higher-Order Moment Matching for Unsupervised Domain Adaptation
نویسندگان
چکیده
منابع مشابه
Robust Unsupervised Domain Adaptation for Neural Networks via Moment Alignment
A novel approach for unsupervised domain adaptation for neural networks is proposed that relies on a metricbased regularization of the learning process. The metric-based regularization aims at domain-invariant latent feature representations by means of maximizing the similarity between domainspecific activation distributions. The proposed metric results from modifying an integral probability me...
متن کاملMoment Matching Theorems for Dimension Reduction of Higher-Order Dynamical Systems via Higher-Order Krylov Subspaces
Moment matching theorems for Krylov subspace based model reduction of higherorder linear dynamical systems are presented in the context of higher-order Krylov subspaces. We introduce the definition of a nth-order Krylov subspace Kn k ({Ai} n i=1;u) based on a sequence of n square matrices {Ai}i=1 and vector u. This subspace is a generalization of Krylov subspaces for higher-order systems, incor...
متن کاملA new unscented Kalman filter with higher order moment-matching
This paper is concerned with filtering nonlinear multivariate time series. A new approximate Bayesian algorithm is proposed which generates sample points and corresponding probability weights that match exactly the predicted values of average marginal skewness and average marginal kurtosis of the unobserved state variables, in addition to matching their mean and the covariance matrix. The perfo...
متن کاملDeep Unsupervised Domain Adaptation for Image Classification via Low Rank Representation Learning
Domain adaptation is a powerful technique given a wide amount of labeled data from similar attributes in different domains. In real-world applications, there is a huge number of data but almost more of them are unlabeled. It is effective in image classification where it is expensive and time-consuming to obtain adequate label data. We propose a novel method named DALRRL, which consists of deep ...
متن کاملBoosting for Unsupervised Domain Adaptation
To cope with machine learning problems where the learner receives data from different source and target distributions, a new learning framework named domain adaptation (DA) has emerged, opening the door for designing theoretically well-founded algorithms. In this paper, we present SLDAB, a self-labeling DA algorithm, which takes its origin from both the theory of boosting and the theory of DA. ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Proceedings of the AAAI Conference on Artificial Intelligence
سال: 2020
ISSN: 2374-3468,2159-5399
DOI: 10.1609/aaai.v34i04.5745