Multi-Source Distilling Domain Adaptation
نویسندگان
چکیده
منابع مشابه
A survey of multi-source domain adaptation
In many machine learning algorithms, a major assumption is that the training and the test samples are in the same feature space and have the same distribution. However, for many real applications this assumption does not hold. In this paper, we survey the problem where the training samples and the test samples are from different distributions. This problem can be referred as domain adaptation. ...
متن کاملMulti-Source Domain Adaptation: A Causal View
This paper is concerned with the problem of domain adaptation with multiple sources from a causal point of view. In particular, we use causal models to represent the relationship between the features X and class label Y , and consider possible situations where different modules of the causal model change with the domain. In each situation, we investigate what knowledge is appropriate to transfe...
متن کاملA Two-Stage Weighting Framework for Multi-Source Domain Adaptation
Discriminative learning when training and test data belong to different distributions is a challenging and complex task. Often times we have very few or no labeled data from the test or target distribution but may have plenty of labeled data from multiple related sources with different distributions. The difference in distributions may be both in marginal and conditional probabilities. Most of ...
متن کاملMulti-Source Domain Adaptation Using Approximate Label Matching
Domain adaptation, and transfer learning more generally, seeks to remedy the problem created when training and testing datasets are generated by different distributions. In this work, we introduce a new unsupervised domain adaptation algorithm for when there are multiple sources available to a learner. Our technique assigns a rough labeling on the target samples, then uses it to learn a transfo...
متن کاملMulti-Source Iterative Adaptation for Cross-Domain Classification
Owing to the tremendous increase in the volume and variety of user generated content, train–once– apply–forever models are insufficient for supervised learning tasks. Thus, developing algorithms that adapt across domains by leveraging data from multiple domains is critical. However, existing adaptation algorithms often fail to identify the right sources to use for adaptation. In this work, we p...
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.v34i07.6997