منابع مشابه
Domain Adaptation with Regularized Optimal Transport
We present a new and original method to solve the domain adaptation problem using optimal transport. By searching for the best transportation plan between the probability distribution functions of a source and a target domain, a non-linear and invertible transformation of the learning samples can be estimated. Any standard machine learning method can then be applied on the transformed set, whic...
متن کاملDeepJDOT: Deep Joint distribution optimal transport for unsupervised domain adaptation
In computer vision, one is often confronted with problems of domain shifts, which occur when one applies a classifier trained on a source dataset to target data sharing similar characteristics (e.g. same classes), but also different latent data structures (e.g. different acquisition conditions). In such a situation, the model will perform poorly on the new data, since the classifier is speciali...
متن کاملSample-oriented Domain Adaptation for Image Classification
Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. The conventional image processing algorithms cannot perform well in scenarios where the training images (source domain) that are used to learn the model have a different distribution with test images (target domain). Also, many real world applicat...
متن کاملTheoretical Analysis of Domain Adaptation with Optimal Transport
Domain adaptation (DA) is an important and emerging field of machine learning that tackles the problem occurring when the distributions of training (source domain) and test (target domain) data are similar but different. Current theoretical results show that the efficiency of DA algorithms depends on their capacity of minimizing the divergence between source and target probability distributions...
متن کاملJoint distribution optimal transportation for domain adaptation
This paper deals with the unsupervised domain adaptation problem, where one wants to estimate aprediction function f in a given target domain without any labeled sample by exploiting the knowledgeavailable from a source domain where labels are known. Our work makes the following assumption: thereexists a non-linear transformation between the joint feature/label space distributio...
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ژورنال
عنوان ژورنال: IEEE Transactions on Pattern Analysis and Machine Intelligence
سال: 2017
ISSN: 0162-8828,2160-9292
DOI: 10.1109/tpami.2016.2615921