Domain Adaptation Deep Learning

Deep discriminative learning for unsupervised domain adaptation information about classi cation.

Domain adaptation deep learning. Therefore multi source domain adaptation mda is needed in order to leverage all of the available data. Domain adaptation by using causal inference to predict invariant conditional distributions. Co regularized alignment for unsupervised domain adaptation.

Under review as a conference paper at iclr 2018 domain adaptation for deep reinforcement learning in visually distinct games anonymous authors paper under double blind review abstract many deep reinforcement learning approaches use graphical state representations. This approach uses an auxiliary reconstruction task to create a shared representation for each of the domains. For instance the deep reconstruction classification network drcn tries to solve these two tasks simultaneously.

Domain adversarial neural network architecture by ganin et al. A new deep learning model for fault diagnosis with good anti noise and domain adaptation ability on raw vibration signal wei zhang gaoliang peng chuanhao li yuanhang chen and zhujun zhang state key laboratory of robotics and system harbin institute of technology no 92 xidazhi street.

Because the domain shift not only exists between each source and target but also exists among different sources the source combined data from different sources may interfere with each other during the learning process riemer2019learning. Deep credible metric learning for unsupervised domain adaptation person re identi cation guangyi chen 1 2 3 yuhao lu 5 jiwen lu and jie zhou 4 1 department of automation tsinghua university china 2 state key lab of intelligent technologies and systems china 3 beijing national research center for information science and technology china 4 tsinghua shenzhen international graduate school. Domain adversarial neural network dann ganin lempitsky 2015 introduces gradient reversal layer to make the source and target distribution similar.

Domain adaptation is a field associated with machine learning and transfer learning this scenario arises when we aim at learning from a source data distribution a well performing model on a different but related target data distribution. I classification of the source data and ii reconstruction of. Deep learning based machinery fault diagnostics with domain adaptation across sensors at different places abstract.