unsupervised model adaptation method
Exploiting Local Feature Patterns for Unsupervised Domain Adaptation
... domain adaptation and unties weight sharing across do- ...domain adaptation: using all labeled source data and all unlabeled target ...backbone model and the convolutional layers are initial- ized ...
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Fast Easy Unsupervised Domain Adaptation with Marginalized Structured Dropout
... main adaptation method by using a log-bilinear language adaptation ...spectral method to estimate low di- mensional context-specific word representations for sequence ...HMM model to ...
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Implementation of Artificial Neural Network applied for the solution of inverse kinematics of 2-link serial chain manipulator.
... a method of artificial neural network applied for the solution of inverse kinematics of 2-link serial chain ...The method is multilayer perceptrons neural network has ...This unsupervised ...
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Unsupervised adaptation of PLDA models for broadcast diarization
... novel model adaptation approach to deal with data variability for speaker diarization in a broadcast ...supervised model adaptation ...an unsupervised adaptation method ...
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Automatic Query Generation and Query Relevance Measurement for Unsupervised Language Model Adaptation of Speech Recognition
... Their method reduces the cost of gathering the adaptation data, but is effective only for those sentences with fixed expressions observed in a question-answering ...a method that uses manually ...
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Re-weighted Adversarial Adaptation Network for Unsupervised Domain Adaptation
... based method[13], we argue that re- ducing the domain shift caused by the background images in MNIST-M is easier than reducing the one between MNIST and SVHN if a generative model is ...generative ...
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Multi Domain Neural Machine Translation through Unsupervised Adaptation
... this unsupervised multi-domain setting, we explore an ef- ficient instance-based adaptation method that, by exploiting the similarity between the training instances and each test sen- tence, ...
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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 utilizes these representations ...
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Evaluating Unsupervised Language Model Adaptation Methods for Speaking Assessment
... LM adaptation methods to automated speech scoring systems that require few, if any, hu- man transcripts, which are expensive and slow to obtain for large-sized adaptation data ...the unsupervised ...
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Unsupervised Model Adaptation using Information Theoretic Criterion
... the method assumes a prior which prefers minimal class ...an unsupervised non-parametric clustering method and is shown to result in significant improvement over k-mean, hier- archical clustering and ...
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Distributionally Robust Learning for Unsupervised Domain Adaptation
... (DRL) method for unsupervised do- main adaptation (UDA) that scales to modern computer-vision ...the model from being overconfident on target inputs far away from the source ...
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Unsupervised Language Model Adaptation Incorporating Named Entity Information
... One method is supervised LM adaptation, where topic information is typically available and a topic specific LM is interpolated with the generic LM (Kneser and Steinbiss, 1993; Suzuki and Gao, ...various ...
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Unsupervised Domain Adaptation of Contextualized Embeddings for Sequence Labeling
... state-of-the-art unsupervised domain adaptation method (Yang and Eisenstein, 2016), without taking any explicit steps to adapt to the target domain of Early Mod- ern ...
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Unsupervised detector adaptation by joint dataset feature learning
... Abstract. Object detection is an important step in automated scene un- derstanding. Training state-of-the-art object detectors typically require manual annotation of training data which can be labor-intensive. In this ...
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Unsupervised Domain Adaptation Based on Source-Guided Discrepancy
... domain adaptation, which has been studied extensively (Ben-David et ...domain adaptation is to find a classifier for a label-scarce target do- main by exploiting a label-rich source ...
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Graph Adaptive Knowledge Transfer for Unsupervised Domain Adaptation
... domain adaptation methods manage to simultaneously build deep architec- tures and conduct knowledge ...our model could further outperform those deep domain adaptation models, ...our model is ...
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Solving nonlinear Lane-Emden type equations with unsupervised combined artificial neural networks
... Marzban et al. [21] used a method based upon hybrid function approximations. They used the properties of hybrid of block-pulse functions and Lagrange interpolating polynomials together for solving the nonlinear ...
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Object landmark discovery through unsupervised adaptation
... our method consists of directly fine-tuning the pre-trained network on the target ...our method. We attribute the improvement obtained by our method to the fact that the core network is not updated ...
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Effective Utterance Classification with Unsupervised Phonotactic Models
... Different possible classification algorithms can be used in our utterance classification method. For the experiments reported here we use the BoosTexter classifier (Schapire and Singer, 2000). Among the ...
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NEER: An Unsupervised Method for Named Entity Evolution Recognition
... an unsupervised method for named entity evolution recognition independent of external knowledge ...co-occurrence method we capture evolving terms in the same ...
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