[PDF] Top 20 Unsupervised Domain Adaptation Based on Source-Guided Discrepancy
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Unsupervised Domain Adaptation Based on Source-Guided Discrepancy
... Note that the above difference of the theoretical guaran- tees does not come from the inherent difference of these es- timators. This is because we adopted the analysis based on the Rademacher complexity, which ... See full document
8
Adaptation Based on Generalized Discrepancy
... each domain was used for training and 10 labeled points from the target distribution were used to select H 00 ...target domain are reported in Figure ...the source is equal to ...the ... See full document
30
Unsupervised Domain Adaptation for Clinical Negation Detection
... target domain labeled ...on source data on the target data, and adding confidently classified target instances to the training data, labeled with whatever the classifier ...instances based on the ... See full document
6
Part of Speech Tagging for Historical English
... Domain adaptation Early work on domain adap- tation focuses on supervised setting, in which some amount of labeled instances are available in the target domain (Jiang and Zhai, 2007; Daum´e ... See full document
11
Unsupervised Domain Adaptation of Contextualized Embeddings for Sequence Labeling
... (1) If this marsch waulle (marsh wall) were not kept, and the canales of eche partes of Sowey river kept from abundance of wedes, al the plaine marsch ground at so- daine raynes (sudden rains) wold be over- flowen, and ... See full document
11
Exploiting Local Feature Patterns for Unsupervised Domain Adaptation
... Distance Guided Representation Learning (WD-GRL) (Shen et ...additional domain discrimina- ...deep domain adaptation and unties weight sharing across do- ...of domain-invariant ...vised ... See full document
8
Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation
... main adaptation in computer vision and machine learning ...distribution discrepancy across different domains. Due to the fact that all the domain alignment approaches can only reduce, but not remove ... See full document
8
Unsupervised Domain Adaptation for Joint Segmentation and POS Tagging
... The combined method One explanation for the comparatively less effect of the character clustering method compared to the self-training method is that, although data sparsity is re- duced, the weights to the ... See full document
10
Task Refinement Learning for Improved Accuracy and Stability of Unsupervised Domain Adaptation
... the source and the target domains, reasoning that the hidden representation of such a model should be less noisy and hence robust to domain ...Models based on variational AEs (Kingma and Welling, ... See full document
12
Importance weighting and unsupervised domain adaptation of POS taggers: a negative result
... the source and target domain (the unlabeled text that comes with the SANCL data), we mark whether it comes from the source or target do- main and train a binary classifier (logistic regres- sion) to ... See full document
6
Simplified Neural Unsupervised Domain Adaptation
... neural-network-based domain adap- tation algorithms have been successful, including domain adversarial methods (Ganin et ...the source and target ...the source in- stances are first ... See full document
6
A Hassle Free Unsupervised Domain Adaptation Method Using Instance Similarity Features
... Domain adaptation aims to use labeled data from a source domain to help build a system for a target domain, possibly with a small amount of labeled data from the target ...target ... See full document
6
Self-Adaptation for Unsupervised Domain Adaptation
... Mean Discrepancy (MMD) (Gretton et ...proposed Domain Adversarial Neural Network (DANN) to learn features that combine the dis- criminative power of a classifier and the domain- invariance of the ... See full document
10
Unsupervised Domain Adaptation by Matching Distributions Based on the Maximum Mean Discrepancy via Unilateral Transformations
... all domain pairs for the two datasets obtained by the proposed methods with different ...many domain pairs with both datasets (14 of 21 cases), the proposed method with the RBF kernel tended to perform ... See full document
8
Unsupervised Domain Adaptation based on Text Relatedness
... The augmented feature space that has been extracted as described in the previous subsection, contains all features of the source domain , and new features from the target domain, each of which is ... See full document
7
Multi Domain Neural Machine Translation through Unsupervised Adaptation
... without domain in- ...this unsupervised multi-domain setting, we explore an ef- ficient instance-based adaptation method that, by exploiting the similarity between the training ... See full document
11
Transductive Adaptation of Black Box Predictions
... methods. Source data are often a sub- ject of legal, technical and contractual constraints between data owners and data ...raw source database are allowed whereas, in our case, only requests for predict- ... See full document
6
Self training improves Recurrent Neural Networks performance for Temporal Relation Extraction
... Our hypothesis is that the range of interesting phenomena found in clinical data is much broader than what is covered by available gold standard datasets for temporal information extraction. The results of Clinical ... See full document
12
Adaptive Ensembling: Unsupervised Domain Adaptation for Political Document Analysis
... domain adaptation. We develop adap- tive ensembling, an unsupervised domain adapta- tion framework that learns from a single-source, labeled corpus (the source domain) and ... See full document
13
Unsupervised Adversarial Domain Adaptation for Implicit Discourse Relation Classification
... Resources for implicit discourse relations are scarce compared to the explicit ones, since they are harder to annotate (Miltsakaki et al., 2004). For example, among corpora annotated with dis- course relations such as ... See full document
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