Domain Adaptation Using Neural Network Joint Model

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Domain adaptation using neural network joint model

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We explore neural joint models for the task of domain adaptation in machine translation in two ways: (i) we apply state-of-the-art domain adaptation techniques, such as mixture modelling and data selection using the recently proposed Neural Network Joint Model (NNJM) (Devlin et al., 2014); (ii) we propose two novel approaches to perform adaptation

1. 3
Publish Year: 2017
Author: Shafiq Joty, Nadir Durrani, Hassan Sajjad, Ahmed Abdelali

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Domain Adaptation Using Neural Network Joint Model

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We explore neural joint models for the task of domain adaptation in machine translation in two ways: (i) we apply state-of-the-art domain adaptation techniques, such as mixture modelling and data selection using the recently proposed Neural Network Joint Model (NNJM) (Devlin et al., 2014); (ii) we propose two novel approaches to perform adaptation through instance …

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(PDF) Domain Adaptation Using Neural Network Joint …

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Abstract. We explore neural joint models for the task of domain adaptation in machine translation in two ways: (i) we apply state-of-the-art domain adaptation techniques, such as mixture modelling

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Domain Adaptation with Adversarial Neural Networks and

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DAP: Domain Adaptation with Adversarial Neural Networks and Auto-encoders 3 A Theoretical Model for Domain Adaptation In Sec. 1 we brie y mention that in order to hope for a successful domain adaptation algorithm, source domain and target domain should be similar to each other in a certain sense. Clearly we need

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Domain Adaptation for Convolutional Neural Networks-Based

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As a result, simply applying convolutional neural networks (CNN) trained on source domain cannot accurately classify the images on target domain. Domain adaptation (DA) can be helpful to solve this problem. In this letter, we design a subspace alignment (SA) and CNN-based framework to solve the DA problem in RS scene image classification.

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Domain Adaptation for Relation Extraction with Domain

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labeled target-domain examples with neural net-works. To our best knowledge, this is the rst work on neural networks for domain adaptation of relation extraction. 3 Model We formulate the relation extraction task as a classication problem over all entity pairs (re-lation candidates) in a sentence. The overall structure of the model is shown in

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 See Also: Recurrent Neural Network Language Model Adaptation For

BDANN : BERT‑based domain adaptation neural network for

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to-end model, named BERT-based domain adaptation neural network (BDANN). BDANN comprises three main modules: a multi-modal feature extractor, a domain classifier and a fake news detector. Specifically, the multi-modal feature extractor employs the pretrained BERT base model to extract text features

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Associative Domain Adaptation - CVF Open Access

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Associative Domain Adaptation Philip Haeusser1,2 [email protected] Thomas Frerix1 [email protected] Alexander Mordvintsev2 [email protected] Daniel Cremers1 [email protected] 1Dept. of Informatics, TU Munich 2Google, Inc. Abstract We propose associative domain adaptation, a novel tech-nique for end-to-end domain adaptation with neural

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Domain Adaptation for Neural Machine Translation

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JianfengGao. Domain adaptation via pseudo in-domain data selection. EMNLP 2011 Kevin Duh, Graham Neubig, Katsuhito Sudoh, and Hajime Tsukada. Adaptation data selection using neural language models: Experiments in machine translation. ACL 2013 MarcinJunczys-Dowmunt. Dual conditional cross-entropy filtering of noisy parallel corpora. WMT 2018

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Neural Domain Adaptation with Contextualized Character

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The document type adaptation is to use one domain document data to label the other domain document, such as using newswire document to label novel document. In this paper, we focus on the document type adaptation. Most previous research [20,19] is based on the hand-crafted model which is difficult and time-consuming. In this paper,

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Contrastive Adaptation Network for Unsupervised Domain

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outputs of layer l in a deep neural network Φθ for the in-put x, where φ(·) denotes the mapping defined by the deep neural network from the input to a specific layer. In the rest of this section, we start our discussions by briefly reviewing the relevant concepts in MMD in Section 3.1. Section 3.2 introduces a new domain discrepancy met-ric.

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Domain Adaptation With Neural Embedding Matching - IEEE

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Abstract: Domain adaptation aims to exploit the supervision knowledge in a source domain for learning prediction models in a target domain. In this article, we propose a novel representation learning-based domain adaptation method, i.e., neural embedding matching (NEM) method, to transfer information from the source domain to the target domain

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(PDF) Event Detection and Domain Adaptation with

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Using convolutional neural network models, we measure the similarity of entity mentions with entities in the knowledge base (KB) and the similarity of relation patterns and relations in the KB.

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[1911.02655v1] Towards Domain Adaptation from Limited Data

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This paper explores domain adaptation for enabling question answering (QA) systems to answer questions posed against documents in new specialized domains. Current QA systems using deep neural network (DNN) technology have proven effective for answering general purpose factoid-style questions. However, current general purpose DNN models tend to be ineffective for use

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transferlearning/transfer_learning_application.md at

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Restyle data using domain adaptation; -18 Document Image Classification with Intra-Domain Transfer Learning and Stacked Generalization of Deep Convolutional Neural Networks. English: Document image classification using transfer learning Deep Transfer Network with Joint Distribution Adaptation:

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[1505.07818] Domain-Adversarial Training of Neural Networks

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Domain-Adversarial Training of Neural Networks. We introduce a new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions. Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved

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Domain-Adversarial Training of Neural Networks

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(document sentiment analysis and image classi cation), where state-of-the-art domain adaptation performance on standard benchmarks is achieved. We also validate the ap-proach for descriptor learning task in the context of person re-identi cation application. Keywords: domain adaptation, neural network, representation learning, deep learning,

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 See Also: Unsupervised Neural Domain Adaptation For Document Image

Domain-adversarial training of neural networks - The

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Dlid: Deep learning for domain adaptation by interpolating between domains. In ICML Workshop on Challenges in Representation Learning, 2013. Google Scholar; Dan Ciresan, Ueli Meier, Jonathan Masci, and Jürgen Schmidhuber. Multi-column deep neural network for traffic sign classification. Neural Networks, 32:333-338, 2012. Google Scholar

1. 104
Publish Year: 2016
Author: GaninYaroslav, UstinovaEvgeniya, AjakanHana, GermainPascal, LarochelleHugo, LavioletteFrançois, Marc...

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Domain Adaptation for Convolutional Neural Networks Based

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As a result, simply applying Convolutional Neural Networks (CNN) trained on source domain cannot accurately classify the images on target domain. Domain Adaptation (DA) can be helpful to solve this problem. In this paper, we design a Subspace Alignment (SA) and CNN based framework to solve the DA problem in RS scene image classification.

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Frustratingly Easy Neural Domain Adaptation

4 hours ago Microsoft.com Visit Site

Domain adaptation and multi-taking with neural networks have been an active research area. We discuss some examples of previous works and how our work differs. Many past approaches to domain adaptation simply augment the network with a parameter that acti-vates on the current domain.

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Domain-Adversarial Training of Neural Networks · Issue #6

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The approach implements this idea in the context of neural network architectures that are trained on labeled data from the source domain and unlabeled data from the target domain (no labeled target-domain data is necessary). As the training progresses, the approach promotes the emergence of features that are (i) discriminative for the main

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Thirtieth AAAI Conference on Artificial Intelligence

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Gated Neural Networks for Targeted Sentiment Analysis / 3087 Meishan Zhang, Yue Zhang, Duy-Tin Vo. A Joint Model for Question Answering over Multiple Knowledge Bases / 3094 Yuanzhe Zhang, Shizhu He, Kang Liu, Jun Zhao. A Joint Model for Entity Set Expansion and Attribute Extraction from Web Search Queries / 3101 Zhenzhong Zhang, Le Sun, Xianpei Han

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Domain Adaptation for Neural Networks by Parameter

4 hours ago Deepai.org Visit Site

For neural networks, there are two standard methods for supervised domain adaptation [Mou et al.2016].The first method is fine tuning: we first train the model with the source dataset and then tune it with the target domain dataset [Venugopalan et al.2015, Kim2014]Since the objective function of neural network training is non-convex, the performance of the trained model

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Domain-Adversarial Training of Neural Networks - Papers

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Domain-Adversarial Training of Neural Networks. 28 May 2015 · Yaroslav Ganin , Evgeniya Ustinova , Hana Ajakan , Pascal Germain , Hugo Larochelle , François Laviolette , Mario Marchand , Victor Lempitsky ·. Edit social preview. We introduce a new representation learning approach for domain adaptation, in which data at training and test time

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Domain adaptation - Wikipedia

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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. For instance, one of the tasks of the common spam filtering problem consists in adapting a model from one

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Simplified Neural Unsupervised Domain Adaptation

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2. Background. This work builds off of existing work in unsupervised domain adaptation, starting with Blitzer’s work on structural correspondence learning (SCL) (Blitzer et al., 2006, 2007).In the UDA task setup, one is given two datasets, the source D S = {X s, y s}, with labels for each instance, and the target D T = {X t}, with unlabeled instances only.

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Zero-Shot Learning Through Cross-Modal Transfer

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by a neural network model [15]. Word vectors capture distributional similarities from a large, unsu- Domain Adaptation. Domain adaptation is useful in situations in which there is a lot of training word vectors from the unsupervised model of Huang et al. [15]. Using free Wikipedia text, their model learns word vectors by predicting how

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Domain-Adversarial Training of Neural Networks - NASA/ADS

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Domain-Adversarial Training of Neural Networks. We introduce a new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions. Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved

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Domain adaptation-based transfer learning using

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Domain adaptation is a well-known technique associated with TL which seeks the same goal in machine learning problems, especially pattern recognition. The goal of a domain adaptation approach is to learn and find transformations which can map both source and target domains into a common feature space.

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Multiple Source Domain Adaptation with Adversarial

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We compare Hard-Max and Soft-Max of MDANs with five baselines: i). best-Single-Source. A basic network trained on each source domain ( 20,000 images) without domain adaptation and tested on the target domain. Among the three models, we report the one achieves the best performance on the test set. ii). Combine-Source.

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Deep transfer neural network using hybrid representations

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In this section, we will first analyze the difference between two aforementioned domain discrepancy representations, and then elicit the motivation of using hybrid representations in domain adaptation which could integrate the advantages of MMD and H-divergence.Next, a transfer network architecture based on both MMD and H-divergence, and …

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Deep Learning for Computer Vision: Transfer Learning and

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Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.

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[PDF] Self-ensembling for domain adaptation - Semantic Scholar

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This paper explores the use of self-ensembling with random image augmentation [11] – a technique that has achieved impressive results in the area of semi-supervised learning – for visual domain adaptation problems. We modify the approach of Laine et al.to improve stability and ease of use. Our approach demonstrates state of the art results when performing adaptation

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Domain-adversarial training of neural networks - The

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Dlid: Deep learning for domain adaptation by interpolating between domains. In ICML Workshop on Challenges in Representation Learning, 2013. Google Scholar; Dan Ciresan, Ueli Meier, Jonathan Masci, and Jürgen Schmidhuber. Multi-column deep neural network for traffic sign classification. Neural Networks, 32:333-338, 2012. Google Scholar

1. 104
Publish Year: 2016
Author: GaninYaroslav, UstinovaEvgeniya, AjakanHana, GermainPascal, LarochelleHugo, LavioletteFrançois, Marc...

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Towards Domain Adaptation from Limited Data for Question

7 hours ago Microsoft.com Visit Site

This paper explores domain adaptation for enabling question answering (QA) systems to answer questions posed against documents in new specialized domains. Current QA systems using deep neural network (DNN) technology have proven effective for answering general purpose factoid-style questions. However, current general purpose DNN models tend to be ineffective for use

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A Neural Model for Joint Event Detection and Summarization

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tion and summarization, we present a neural network model for integrating event filtering, clustering, and summarization jointly. To our knowledge, this is the first work to employ neural networks for collaborative event detection and sum-marization. 2.2 Neural Joint Modeling There has been a line of research using discrete models to

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Reducing model bias in a deep learning classifier using

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Unfortunately, simply training a deep neural network on images from one domain does not guarantee satisfactory performance on new images from a different domain. The ability to share cross-domain knowledge is the main advantage of modern deep domain adaptation techniques.

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2021 International Joint Conference on Neural Networks

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Abstract. HTML. Capsule network (CapsNet) is a novel neural network architecture that overcame the drawback of loss of poses and position caused in convolutional neural networks and achieves better results than convolutional neural networks in some tasks. However, CapsNet computing efficiency needs to be improved.

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Feature Based Domain Adaptation for Neural Network

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Feature Based Domain Adaptation for Neural Network Language Models with Factorised Hidden Layers

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(PDF) Domain knowledge acquisition for Information

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Domain Knowledge Acquisition for Information Retrieval using Neural Networks Fabio Crestani Department of Computing Science University of Glasgow Scotland - UK Abstract This paper presents the results of some experiments investigating the use of Neural Networks in the learning engine of an Connectionist Informa- tion Retrieval system called CIRS.

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0#123%+ 45+6.#-+ !78%*+

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c 2015 The Association for Computational Linguistics Order print-on-demand copies from: Curran Associates 57 Morehouse Lane Red Hook, New York 12571

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Transfer Learning and Domain Adaptation (DLAI D5L2 2017

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Fine-tuning: supervised domain adaptation Train deep net on “nearby” task for which it is easy to get labels using standard backprop E.g. ImageNet classification Pseudo classes from augmented data Slow feature learning, ego-motion Cut off top layer(s) of network and replace with supervised objective for target domain Fine-tune network using

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Which is the best neural network for domain adaptation?

20191111 arXiv Towards Domain Adaptation from Limited Data for Question Answering Using Deep Neural Networks 20191111 arXiv SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization

How is domain adversarial training of neural networks inspired?

Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot discriminate between the training (source) and test (target) domains. ..

How is domain adaptation used in machine learning?

Domain adaptation is a sub-discipline of machine learning which deals with scenarios in which a model trained on a source distribution is used in the context of a different (but related) target distribution. In general, domain adaptation uses labeled data in one or more source domains to solve new tasks in a target domain.

What are the approaches to Deep Domain adaptation?

There are multiple approaches to domain adaptation. In “shallow” (not deep) domain adaptation, two methods are commonly used: reweighing the source samples and training on the reweighed samples and trying to learn a shared space to match the distributions of the source and target datasets.

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