In this paper , we propose to leverage crowdsourced GPS data to improve and support road extraction from aerial imagery . Through novel data augmentation , GPS rendering , and 1D transpose convolution techniques , we show almost 5 % improvements over previous competition winning models , and much better robustness when predicting new areas without any new training data or domain adaptation . However , in real applications , this condition may be violated , which causes degradation of classification accuracy . Domain adaptation is an effective approach to address this problem . In this paper , we propose a general domain adaptation framework from the perspective of prediction reweighting , from which a novel approach is derived . But , how can a robot automatically understand what is useful among a large amount of information and perform knowledge transfer ? In this paper we address the domain adaptation problem in the context of visual place recognition . We consider the scenario where a robot equipped with a monocular camera explores a new environment . In this paper , we attempt to solve the domain adaptation problem for deep stereo matching networks . Instead of resorting to black-box structures or layers to find implicit connections across domains , we focus on investigating adaptation gaps for stereo matching . Unfortunately , this assumption is mostly invalid in real application , resulting in a certain lack of applicability for the traditional diagnosis approaches . Inspired by the idea of transfer learning that leverages the knowledge learnt from rich labeled data in source domain to facilitate diagnosing a new but similar target task , a new intelligent fault diagnosis framework , i.e. , deep transfer network ( DTN ) , which generalizes deep learning model to domain adaptation scenario , is proposed in this paper . By extending the marginal distribution adaptation ( MDA ) to joint distribution adaptation ( JDA ) , the proposed framework can exploit the discrimination structures associated with the labeled data in source domain to adapt the conditional distribution of unlabeled target data , and thus guarantee a more accurate distribution matching . In this work , we propose to improve the current CNN models for ED by introducing the non-consecutive convolution . Our systematic evaluation on both the general setting and the domain adaptation setting demonstrates the effectiveness of the nonconsecutive CNN model , leading to the significant performance improvement over the current state-of-the-art systems . Manually labeling data can be costly and time consuming . An alternative is to learn classifiers in a domain adaptation setting in which existing labeled data can be leveraged from a related problem , referred to as source domain , in conjunction with a small amount of labeled data and large amount of unlabeled data for the problem of interest , or target domain . In this paper , we propose two similar domain adaptation classifiers based on a na¨A±ve Bayes algorithm . In this paper , we present a new approach called Domain Adversarial network with Representation Learning ( DARL ) , to improve domain adaptation by introducing an encoding layer as part of DARL model learning . We integrate a Stacked Denoising Autoencoder and Adversarial learning for the domain adaptation process . The advantage of the proposed method is that it can extract descriptive features under noisy conditions while still learning task discriminative features . Under limited data , our implementation of an IBQA system achieves good performance , improves with additional training instances , and is easily trainable and adaptable to new types of data . The IBQA approach provides a principled , robust , and easy to implement base system which constitutes a robust and well performing platform for further domain-specific adaptation . We report significant improvement both when the seed and test data are in the same domain and in the outof-domain adaptation scenario . In particular , we achieve 50 % reduction in annotation cost for the in-domain case , yielding an improvement of 66 % over previous work , and a 20 - 33 % reduction for the domain adaptation case . This is the first time that self-training with small labeled datasets is applied successfully to these tasks . This contribution studies an approach based on dictionary learning which enables the alignment of the sparse representations of two images . Set in a domain adaptation context , the purpose of this work is to re-synthesize the pixels of a remote sensing image so that , for a given land-cover class , the new values of the samples are comparable across acquisitions . Consequently , the data space of a given source image can be converted to that of a related target image , or vice-versa . We took part in four translation directions , en-de , deen , en-fr , and fr-en . The models have been trained with an attentional encoder-decoder model using Nematus , training data filtering and back-translation have been applied for domain-adaptation purposes . In this paper , we introduce a new domain adaptation ( DA ) algorithm where the source and target domains are represented by subspaces described by eigenvectors . In this context , our method seeks a domain adaptation solution by learning a mapping function which aligns the source subspace with the target one . We show that the solution of the corresponding optimization problem can be obtained in a simple closed form , leading to an extremely fast algorithm . We evaluate our method on digit recognition and sentiment analysis datasets . Our proposed method achieves state - of - the - art performance on the benchmark digit recognition datasets of domain adaptation . section : Introduction We evaluate the accuracy of target - specific networks in all experiments . Visual Domain Adaptation For visual domain adaptation , we perform our evaluation on the digits datasets and traffic signs datasets . The dataset contains reviews on four types of products : books , DVDs , electronics , and kitchen appliances . We evaluate our method on 12 domain adaptation scenarios . The results are shown in Table 1 . However , the intrinsic domain difference between synthetic and real images usually causes a significant performance drop when applying the learned model to real world scenarios . This is mainly due to two reasons : 1 ) the model overfits to synthetic images , making the convolutional filters incompetent to extract informative representation for real images ; 2 ) there is a distribution difference between synthetic and real data , which is also known as the domain adaptation problem . To this end , we propose a new reality oriented adaptation approach for urban scene semantic segmentation by learning from synthetic data . We use to balance the two strengths in training and superscript to index different types of label distributions . Note that in the domain adaptation context , we actually can not directly compute the label distribution from the groundtruth annotations of the target domain . Nonetheless , estimating them using the labeled source data is easier than assigning labels to every single pixel of the target images . Some applications include weakly supervised segmentation and detection , and rule - regularized training of neural networks . In addition to the domain adaptation setting and novel target properties , another key distinction of our work is that we decouple the label distributions from the network predictions and thus avoid the EM type of optimizations . Our approach learns the segmentation network with almost effortless changes to the popular deep learning tools . paragraph : Comparison with learning domain - invariant features . At our first attempt to solve the domain adaptation problem for the semantic segmentation of urban scenes , we tried to learn domain invariant features following the deep domain adaptation methods for classification . In particular , we impose the maximum mean discrepancy over the layer before the output . Several papers , however , observed that the performance of the resulting re - identification systems drops very considerably when descriptors trained on one data set and tested on another . It is therefore natural to handle such cross - domain evaluation as a domain - adaptation problem , where each camera network ( data set ) constitutes a domain . Recently , several papers with significantly improved re - identification performance [ reference ][ reference ][ reference ] have been presented , with [ reference ] reporting good results in cross - data - set evaluation scenario . We here report on new experiments that we have carried out in order to test PyDCI , and in which we use as baselines new high - performing methods that have appeared after DCI was originally proposed . These experiments show that , thanks to a few subtle ways in which we have improved DCI , PyDCI outperforms both JaDCI and the above - mentioned high - performing methods , and delivers the best known results on the two popular benchmarks on which we had tested DCI , i.e. , MultiDomainSentiment ( a.k.a . MDS – for cross - domain adaptation ) and Webis - CLS - 10 subsection : Effectiveness on Cross - Domain Cross - Lingual Classification Table [ reference ] reports classification accuracy values obtained in the domain adaptation setting proposed in , in which both domain and language differ between the source and target ( i.e. , when the classification task is simultaneously cross - domain and cross - lingual ) . In Table [ reference ] we include the results we had obtained in for the Cross - Lingual Structural Correspondence Learning ( SCL ) method ( which we use here as a baseline ) , using its authors ’ code ( see ) . These are general - purpose semi - supervised algorithms that treat the model as a black box and can thus be used easily - with a few additions - with the current generation of NLP models . Many of these methods , though , were originally developed with in - domain performance in mind , so their effectiveness in a domain adaptation setting remains unexplored . In particular , we re - evaluate three traditional bootstrapping methods , self - training [ reference ] , tri - training [ reference ] , and tritraining with disagreement [ reference ] for neural network - based approaches on two NLP tasks with different characteristics , namely , a sequence prediction and a classification task ( POS tagging and sentiment analysis ) . This approach may be regarded as a “ first - order ” approximation to our approach , which seeks a tighter alignment between distributions . section : Deep Domain Adaptation subsection : The model In this chapter , we present CORrelation ALignment ( CORAL ) , a simple yet effective method for unsupervised domain adaptation . CORAL minimizes domain shift by aligning the second-order statistics of source and target distributions , without requiring any target labels . Unsupervised domain adaptation , which leverages label information from other domains to solve tasks on a domain without any labels , can alleviate the problem of the scarcity of labels and expensive labeling costs faced by supervised semantic segmentation . In this paper , we utilize adversarial learning and semi-supervised learning simultaneously to solve the task of unsupervised domain adaptation in semantic segmentation . We propose a new approach that trains two segmentation models with the adversarial learning symmetrically and further introduces the consistency between the outputs of the two models into the semi-supervised learning to improve the accuracy of pseudo labels which significantly affect the final adaptation performance . This work addresses the unsupervised domain adaptation problem , especially for the partial scenario where the class labels in the target domain are only a subset of those in the source domain . Such a partial transfer setting sounds realistic but challenging while existing methods always suffer from two key problems , i.e. , negative transfer and uncertainty propagation . In this paper , we propose a transfer domain class clustering ( TDCC ) algorithm to address the unsupervised domain adaptation problem , in which the training data ( source domain ) and the test data ( target domain ) follow different distributions . TDCC aims to derive new feature representations for source and target in a latent subspace to simultaneously reduce the distribution distance between two domains , which helps transfer the source knowledge to the target domain effectively , and enhance the class discriminativeness of data as much as possible by minimizing the intra-class variations , which can benefit the final classification a lot . In this paper , we propose a new approach called Deep LogCORAL for unsupervised visual domain adaptation . Our work builds on the recently proposed Deep CORAL method , which aims to train a convolutional neural network and simultaneously minimize the Euclidean distance of convariance matrices between the source and target domains . We propose a novel approach for unsupervised visual domain adaptation that exploits auxiliary information in a target domain . The key idea is to embed data in the target domain into a subspace where samples are better organized , expecting auxiliary information to serve as a somewhat semantically related signal . For supervised domain adaptation , we propose joint neural network training while allowing the weights to be shared between the source and target streams , and an additional domain regularizer . In the unsupervised domain adaptation scenario , the weights are not shared in order to explicitly model the domain shift . However , the weights are related by weight regularizers to take into account the difference between the two domains . Adversarial learning has demonstrated good performance in the unsupervised domain adaptation setting , by learning domain-invariant representations that perform well on the source domain . However , recent work has underlined limitations of existing methods in the presence of mismatched label distributions between the source and target domains . In this paper we focus on the acoustic scene classification ( ASC ) task and propose an adversarial deep learning method to allow adapting an acoustic scene classification system to deal with a new acoustic channel resulting from data captured with a different recording device . We build upon the theoretical model of ℋΔℋ-distance and previous adversarial discriminative deep learning method for ASC unsupervised domain adaptation , and we present an adversarial training based method using the Wasserstein distance . We improve the state-of-the-art mean accuracy on the data from the unseen conditions from 32 % to 45 % , using the TUT Acoustic Scenes dataset . Our approach is composed of two steps : ( i ) a tumor-aware unsupervised cross-domain adaptation ( CT to MRI ) , followed by ( ii ) semi-supervised tumor segmentation using Unet trained with synthesized and limited number of original MRIs . We introduced a novel target specific loss , called tumor-aware loss , for unsupervised cross-domain adaptation that helps to preserve tumors on synthesized MRIs produced from CT images . In comparison , state-of-the art adversarial networks trained without our tumor-aware loss produced MRIs with ill-preserved or missing tumors . The key issue in unsupervised visual domain adaptation is how to do the feature alignment between source and target domains . Inspired by the adversarial learning in generative adversarial networks , this study proposes a novel adversarial auto-encoder for unsupervised deep domain adaptation . This method incorporates the auto-encoder with the adversarial learning so that the domain similarity and reconstruction information from the decoder can be exploited to facilitate the adversarial domain adaptation in the encoder . The procedure of tackling this discrepancy between the training ( source ) and testing ( target ) domains is known as domain adaptation . We propose an unsupervised version of domain adaptation that considers the presence of only unlabelled data in the target domain . Our approach centres on finding correspondences between samples of each domain . We showed that via enforcing a simple weight - sharing constraint to the layers that are responsible for decoding abstract semantics , the CoGAN learned the joint distribution of images by just using samples drawn separately from the marginal distributions . In addition to convincing image generation results on faces and RGBD images , we also showed promising results of the CoGAN framework for the image transformation and unsupervised domain adaptation tasks . bibliography : References As far as we know , the domain transfer problem we formulate is novel despite being ecological ( i.e. , appearing naturally in the real - world ) , widely applicable , and related to cognitive reasoning thewaywethink . In the discussion below , we survey recent GAN work , compare our work to the recent image synthesis work and make links to unsupervised domain adaptation . GAN gan methods train a generator network that synthesizes samples from a target distribution given noise vectors . Their method is able to invert the mid - level activations of AlexNet and reconstruct the input image . In contrast , we solve the problem of unsupervised domain transfer and apply the loss terms in different domains : pixel loss in the target domain , and feature loss in the source domain . Another class of very promising generative techniques that has recently gained traction is neural style transfer . Unlike those works and similarly to e.g. DLID we use the whole unlabeled target domain ( as the premise of our method is the abundance of unlabeled data in the target domain ) . Under this transductive setting , our method is able to improve previously - reported state - of - the - art accuracy for unsupervised adaptation very considerably ( Table [ reference ] ) , especially in the most challenging Amazon Webcam scenario ( the two domains with the largest domain shift ) . section : Discussion CoGANs liu_arxiv16 jointly learn a source and target representation through explicit weight sharing of certain layers while each source and target has a unique generative adversarial objective . ghifary_eccv16 uses an additional reconstruction objective in the target domain to encourage alignment in the unsupervised adaptation setting . In contrast , another approach is to directly convert the target image into a source style image ( or visa versa ) , largely based on Generative Adversarial Networks ( GANs ) goodfellow_nips14 . section : Cycle - Consistent Adversarial Domain Adaption We consider the problem of unsupervised adaptation , where we are provided source data , source labels , and target data , but no target labels . The goal is to learn a model that can correctly predict the label for the target data . Through this framework we are able to motivate a novel domain adaptation method , ADDA , and offer insight into our design decisions . In the next section we demonstrate promising results on unsupervised adaptation benchmark tasks , studying adaptation across digits and across modalities . section : Experiments We now evaluate ADDA for unsupervised classification adaptation across four different domain shifts . We explore three digits datasets of varying difficulty : MNIST , USPS , and SVHN . subsection : MNIST , USPS , and SVHN digits datasets We experimentally validate our proposed method in an unsupervised adaptation task between the MNIST , USPS , and SVHN digits datasets , which consist 10 classes of digits . Example images from each dataset are visualized in Figure [ reference ] and Table [ reference ] . In contrast , our framework applies to an unsupervised scenario where an obvious partition of the dataset is unknown . Furthermore , extending GMAN to the semi - supervised domain - adaptation scenario would suggest multiple discriminators per domain , therefore our line of research is strictly orthogonal to that of their multi - domain discriminator approach . Also , note that assigning a discriminator to each domain is akin to prescribing a new discriminator to each value of a conditional variable in conditional GANs [ reference ] ) . We consider the development set of ANSWERS as our only target dev set to set hyperparameters . This may result in suboptimal per - domain settings but better resembles an unsupervised adaptation scenario . section : Sentiment analysis Ideally , we wish to have a trained ( locked ) model that can work uniformly well across unseen domains without further training . In this paper , we propose a deep stacked transformation approach for domain generalization . Specifically , a series of $ {n}$ stacked transformations are applied to each image during network training . Domain generalization ( DG ) aims to incorporate knowledge from multiple source domains into a single model that could generalize well on unseen target domains . This problem is ubiquitous in practice since the distributions of the target data may rarely be identical to those of the source data . This limits the practical applicability of FG-SBIR . In this paper , we identify cross-category generalisation for FG-SBIR as a domain generalisation problem , and propose the first solution . Our key contribution is a novel unsupervised learning approach to model a universal manifold of prototypical visual sketch traits . If DA methods are applied directly to DG by a simple exclusion of the target data from training , poor performance will result for a given task . In this paper , we tackle the domain generalization challenge in two ways . In our first approach , we propose a novel deep domain generalization architecture utilizing synthetic data generated by a Generative Adversarial Network ( GAN ) . Experiments about the weakly-supervised semantic segmentation task show the effectiveness of our method . Besides , we further explore and validate that the proposed method is able to improve the generalization ability of a classification network in domain adaptation and domain generalization settings . Our results show that even the state-of-the-art deep learning models show significant performance degradation in the presence of ambient acoustic noise , and more surprisingly under scenarios of microphone variability , with accuracy losses as high as 15 % in some scenarios . Further , we provide intuition on how this problem of model robustness relates to the broader topic of dataset-shift in the machine learning literature , and highlight future research directions for the mobile sensing community which include the investigation of domain adaptation and domain generalization solutions in the context of sensing systems . They are cooperatively trained with standard continuous and discrete optimization schemes . Our model does not require any linguistic structure for supervision and its recursive nature allows for out-of-domain generalization with little loss in performance . Additionally , our approach performs competitively on several natural language tasks , such as Natural Language Inference or Sentiment Analysis . Finally , we introduce parameter sharing techniques to reduce the computation difficulty of our strategy . Empirical results show the superior performance of our proposed method on both toy dataset and well-used domain generalization tasks . Partial domain adaptation aims to tackle this generalization issue where no labeled data are provided in target domain . This paper presents an adversarial learning for partial domain adaptation where a symmetric metric based on the Wasserstein distance is adopted in an adversarial learning objective . We build a Wasserstein partial transfer network where the Wasserstein adversarial objective is jointly optimized to partially transfer the relevance knowledge from source to target domains . Enlarging the Domain Gap in Open Set Domain Adaptation . Unsupervised domain adaptation aims to leverage labeled data from a source domain to learn a classifier for an unlabeled target domain . This approach was developed using a noisy TED Talks corpus and tested on a Wikipedia-based comparable corpus ; however , it can be extended to any text domain or language pair . The proposed method uses various statistical measures for sentence comparison and can be used for in-domain data adaptation tasks as well . Minimization of data loss was ensured by parameter adaptation . We will study the adaptation of the recogniser as an input method for the post-editor , and investigate the improvement of speech transcription for translation purposes . Furthermore we will study how to perform automatic domain-adaptation for speech recognition , in order to automatically adapt the language models of the recogniser to the domain . • Workflows and personalised user interfaces – We aim at a higher comfort and productivity for the translators , by analysing and modelling current translation systems and translator 's workflows and practices , investigating new visualisations of translation features , and developing and testing new interfaces for translation work . The paper presents scheme for doing Domain Adaptation for multiple domains simultaneously . The proposed method segments a large corpus into various parts using self-organizing maps ( SOMs ) . G , and an indirect method with better extrapolation properties . We evaluate our methods on two crossdomain visual recognition benchmarks , where they perform close to the upper bound of full data domain adaptation . This demonstrates that data is not necessary for domain adaptation if a domain can be parametrically described . In-domain adaptation ( DA ) , the knowledge trained in one domain , is used to test an unknown domain . Existing approaches use limited efforts on DA in sentiment classification ( SC ) using neural networks . We show that under practical conditions , it defines a minimax game that can promote the joint distribution alignment . Except for the traditional closed set domain adaptation , we also extend DADA for extremely challenging problem settings of partial and open set domain adaptation . Experiments show the efficacy of our proposed methods and we achieve the new state of the art for all the three settings on benchmark datasets . This paper introduces partial domain adaptation as a new domain adaptation scenario , which relaxes the fully shared label space assumption to that the source label space subsumes the target label space . Previous methods typically match the whole source domain to the target domain , which are vulnerable to negative transfer for the partial domain adaptation problem due to the large mismatch between label spaces . We present Partial Adversarial Domain Adaptation ( PADA ) , which simultaneously alleviates negative transfer by down-weighing the data of outlier source classes for training both source classifier and domain adversary , and promotes positive transfer by matching the feature distributions in the shared label space . This degradation is due to the difference of probability distributions between training data and testing data , known as source data domain and target data domain , respectively . To better adapt a smart camera network to a specific situation , some form of target domain adaptation is needed , which usually requires both the source domain data and as much as possible target domain data at each node , which may strain storage capacity and bandwidth . In this paper , we propose an adaptation method which fuses the source specific hypotheses ( SSHs ) and target specific hypotheses ( TSHs ) - requiring only a pre-trained face detector and a few target data to be shared by peer-to-peer communications , thus relieving the storage and bandwidth constraints . To overcome this limitation , we devise a general forward-backward splitting algorithm based on Bregman distances for solving a wide range of optimization problems involving a differentiable function with Lipschitz-continuous gradient and a doubly stochastic constraint . We illustrate the efficiency of our approach in the context of continuous domain adaptation . Experiments show that the proposed method leads to a significant improvement in terms of speed and performance with respect to the state of the art for domain adaptation on a continually rotating distribution coming from the standard two moon dataset . In this work , we address the scenario in which the target domain is continually , albeit slowly , evolving , and in which , at different time frames , we are given a batch of test data to classify . We exploit the geometry-awareness that optimal transport offers for the resolution of continuous domain adaptation problems . We propose a regularized optimal transport model that takes into account the transportation cost , the entropy of the probabilistic coupling , the labels of the source domain , and the similarity between successive target domains . Our contribution is the first deep architecture that tackles predictive domain adaptation , able to leverage over the information brought by the auxiliary domains through a graph . Moreover , we present a simple yet effective strategy that allows us to take advantage of the incoming target data at test time , in a continuous domain adaptation scenario . Experiments on three benchmark databases support the value of our approach .
In this paper , we propose to leverage crowdsourced GPS data to improve and support road extraction from aerial imagery . Through novel data augmentation , GPS rendering , and 1D transpose convolution techniques , we show almost 5 % improvements over previous competition winning models , and much better robustness when predicting new areas without any new training data or domain adaptation .
However , in real applications , this condition may be violated , which causes degradation of classification accuracy . Domain adaptation is an effective approach to address this problem . In this paper , we propose a general domain adaptation framework from the perspective of prediction reweighting , from which a novel approach is derived .
But , how can a robot automatically understand what is useful among a large amount of information and perform knowledge transfer ? In this paper we address the domain adaptation problem in the context of visual place recognition . We consider the scenario where a robot equipped with a monocular camera explores a new environment .
In this paper , we attempt to solve the domain adaptation problem for deep stereo matching networks . Instead of resorting to black-box structures or layers to find implicit connections across domains , we focus on investigating adaptation gaps for stereo matching .
Unfortunately , this assumption is mostly invalid in real application , resulting in a certain lack of applicability for the traditional diagnosis approaches . Inspired by the idea of transfer learning that leverages the knowledge learnt from rich labeled data in source domain to facilitate diagnosing a new but similar target task , a new intelligent fault diagnosis framework , i.e. , deep transfer network ( DTN ) , which generalizes deep learning model to domain adaptation scenario , is proposed in this paper . By extending the marginal distribution adaptation ( MDA ) to joint distribution adaptation ( JDA ) , the proposed framework can exploit the discrimination structures associated with the labeled data in source domain to adapt the conditional distribution of unlabeled target data , and thus guarantee a more accurate distribution matching .
In this work , we propose to improve the current CNN models for ED by introducing the non-consecutive convolution . Our systematic evaluation on both the general setting and the domain adaptation setting demonstrates the effectiveness of the nonconsecutive CNN model , leading to the significant performance improvement over the current state-of-the-art systems .
Manually labeling data can be costly and time consuming . An alternative is to learn classifiers in a domain adaptation setting in which existing labeled data can be leveraged from a related problem , referred to as source domain , in conjunction with a small amount of labeled data and large amount of unlabeled data for the problem of interest , or target domain . In this paper , we propose two similar domain adaptation classifiers based on a na¨A±ve Bayes algorithm .
In this paper , we present a new approach called Domain Adversarial network with Representation Learning ( DARL ) , to improve domain adaptation by introducing an encoding layer as part of DARL model learning . We integrate a Stacked Denoising Autoencoder and Adversarial learning for the domain adaptation process . The advantage of the proposed method is that it can extract descriptive features under noisy conditions while still learning task discriminative features .
Under limited data , our implementation of an IBQA system achieves good performance , improves with additional training instances , and is easily trainable and adaptable to new types of data . The IBQA approach provides a principled , robust , and easy to implement base system which constitutes a robust and well performing platform for further domain-specific adaptation .
We report significant improvement both when the seed and test data are in the same domain and in the outof-domain adaptation scenario . In particular , we achieve 50 % reduction in annotation cost for the in-domain case , yielding an improvement of 66 % over previous work , and a 20 - 33 % reduction for the domain adaptation case . This is the first time that self-training with small labeled datasets is applied successfully to these tasks .
This contribution studies an approach based on dictionary learning which enables the alignment of the sparse representations of two images . Set in a domain adaptation context , the purpose of this work is to re-synthesize the pixels of a remote sensing image so that , for a given land-cover class , the new values of the samples are comparable across acquisitions . Consequently , the data space of a given source image can be converted to that of a related target image , or vice-versa .
We took part in four translation directions , en-de , deen , en-fr , and fr-en . The models have been trained with an attentional encoder-decoder model using Nematus , training data filtering and back-translation have been applied for domain-adaptation purposes .
In this paper , we introduce a new domain adaptation ( DA ) algorithm where the source and target domains are represented by subspaces described by eigenvectors . In this context , our method seeks a domain adaptation solution by learning a mapping function which aligns the source subspace with the target one . We show that the solution of the corresponding optimization problem can be obtained in a simple closed form , leading to an extremely fast algorithm .
We evaluate our method on digit recognition and sentiment analysis datasets . Our proposed method achieves state - of - the - art performance on the benchmark digit recognition datasets of domain adaptation . section : Introduction
We evaluate the accuracy of target - specific networks in all experiments . Visual Domain Adaptation For visual domain adaptation , we perform our evaluation on the digits datasets and traffic signs datasets .
The dataset contains reviews on four types of products : books , DVDs , electronics , and kitchen appliances . We evaluate our method on 12 domain adaptation scenarios . The results are shown in Table 1 .
However , the intrinsic domain difference between synthetic and real images usually causes a significant performance drop when applying the learned model to real world scenarios . This is mainly due to two reasons : 1 ) the model overfits to synthetic images , making the convolutional filters incompetent to extract informative representation for real images ; 2 ) there is a distribution difference between synthetic and real data , which is also known as the domain adaptation problem . To this end , we propose a new reality oriented adaptation approach for urban scene semantic segmentation by learning from synthetic data .
We use to balance the two strengths in training and superscript to index different types of label distributions . Note that in the domain adaptation context , we actually can not directly compute the label distribution from the groundtruth annotations of the target domain . Nonetheless , estimating them using the labeled source data is easier than assigning labels to every single pixel of the target images .
Some applications include weakly supervised segmentation and detection , and rule - regularized training of neural networks . In addition to the domain adaptation setting and novel target properties , another key distinction of our work is that we decouple the label distributions from the network predictions and thus avoid the EM type of optimizations . Our approach learns the segmentation network with almost effortless changes to the popular deep learning tools .
paragraph : Comparison with learning domain - invariant features . At our first attempt to solve the domain adaptation problem for the semantic segmentation of urban scenes , we tried to learn domain invariant features following the deep domain adaptation methods for classification . In particular , we impose the maximum mean discrepancy over the layer before the output .
Several papers , however , observed that the performance of the resulting re - identification systems drops very considerably when descriptors trained on one data set and tested on another . It is therefore natural to handle such cross - domain evaluation as a domain - adaptation problem , where each camera network ( data set ) constitutes a domain . Recently , several papers with significantly improved re - identification performance [ reference ][ reference ][ reference ] have been presented , with [ reference ] reporting good results in cross - data - set evaluation scenario .
We here report on new experiments that we have carried out in order to test PyDCI , and in which we use as baselines new high - performing methods that have appeared after DCI was originally proposed . These experiments show that , thanks to a few subtle ways in which we have improved DCI , PyDCI outperforms both JaDCI and the above - mentioned high - performing methods , and delivers the best known results on the two popular benchmarks on which we had tested DCI , i.e. , MultiDomainSentiment ( a.k.a . MDS – for cross - domain adaptation ) and Webis - CLS - 10
subsection : Effectiveness on Cross - Domain Cross - Lingual Classification Table [ reference ] reports classification accuracy values obtained in the domain adaptation setting proposed in , in which both domain and language differ between the source and target ( i.e. , when the classification task is simultaneously cross - domain and cross - lingual ) . In Table [ reference ] we include the results we had obtained in for the Cross - Lingual Structural Correspondence Learning ( SCL ) method ( which we use here as a baseline ) , using its authors ’ code ( see ) .
These are general - purpose semi - supervised algorithms that treat the model as a black box and can thus be used easily - with a few additions - with the current generation of NLP models . Many of these methods , though , were originally developed with in - domain performance in mind , so their effectiveness in a domain adaptation setting remains unexplored . In particular , we re - evaluate three traditional bootstrapping methods , self - training [ reference ] , tri - training [ reference ] , and tritraining with disagreement [ reference ] for neural network - based approaches on two NLP tasks with different characteristics , namely , a sequence prediction and a classification task ( POS tagging and sentiment analysis ) .
This approach may be regarded as a “ first - order ” approximation to our approach , which seeks a tighter alignment between distributions . section : Deep Domain Adaptation subsection : The model
In this chapter , we present CORrelation ALignment ( CORAL ) , a simple yet effective method for unsupervised domain adaptation . CORAL minimizes domain shift by aligning the second-order statistics of source and target distributions , without requiring any target labels .
Unsupervised domain adaptation , which leverages label information from other domains to solve tasks on a domain without any labels , can alleviate the problem of the scarcity of labels and expensive labeling costs faced by supervised semantic segmentation . In this paper , we utilize adversarial learning and semi-supervised learning simultaneously to solve the task of unsupervised domain adaptation in semantic segmentation . We propose a new approach that trains two segmentation models with the adversarial learning symmetrically and further introduces the consistency between the outputs of the two models into the semi-supervised learning to improve the accuracy of pseudo labels which significantly affect the final adaptation performance .
This work addresses the unsupervised domain adaptation problem , especially for the partial scenario where the class labels in the target domain are only a subset of those in the source domain . Such a partial transfer setting sounds realistic but challenging while existing methods always suffer from two key problems , i.e. , negative transfer and uncertainty propagation .
In this paper , we propose a transfer domain class clustering ( TDCC ) algorithm to address the unsupervised domain adaptation problem , in which the training data ( source domain ) and the test data ( target domain ) follow different distributions . TDCC aims to derive new feature representations for source and target in a latent subspace to simultaneously reduce the distribution distance between two domains , which helps transfer the source knowledge to the target domain effectively , and enhance the class discriminativeness of data as much as possible by minimizing the intra-class variations , which can benefit the final classification a lot .
In this paper , we propose a new approach called Deep LogCORAL for unsupervised visual domain adaptation . Our work builds on the recently proposed Deep CORAL method , which aims to train a convolutional neural network and simultaneously minimize the Euclidean distance of convariance matrices between the source and target domains .
We propose a novel approach for unsupervised visual domain adaptation that exploits auxiliary information in a target domain . The key idea is to embed data in the target domain into a subspace where samples are better organized , expecting auxiliary information to serve as a somewhat semantically related signal .
For supervised domain adaptation , we propose joint neural network training while allowing the weights to be shared between the source and target streams , and an additional domain regularizer . In the unsupervised domain adaptation scenario , the weights are not shared in order to explicitly model the domain shift . However , the weights are related by weight regularizers to take into account the difference between the two domains .
Adversarial learning has demonstrated good performance in the unsupervised domain adaptation setting , by learning domain-invariant representations that perform well on the source domain . However , recent work has underlined limitations of existing methods in the presence of mismatched label distributions between the source and target domains .
In this paper we focus on the acoustic scene classification ( ASC ) task and propose an adversarial deep learning method to allow adapting an acoustic scene classification system to deal with a new acoustic channel resulting from data captured with a different recording device . We build upon the theoretical model of ℋΔℋ-distance and previous adversarial discriminative deep learning method for ASC unsupervised domain adaptation , and we present an adversarial training based method using the Wasserstein distance . We improve the state-of-the-art mean accuracy on the data from the unseen conditions from 32 % to 45 % , using the TUT Acoustic Scenes dataset .
Our approach is composed of two steps : ( i ) a tumor-aware unsupervised cross-domain adaptation ( CT to MRI ) , followed by ( ii ) semi-supervised tumor segmentation using Unet trained with synthesized and limited number of original MRIs . We introduced a novel target specific loss , called tumor-aware loss , for unsupervised cross-domain adaptation that helps to preserve tumors on synthesized MRIs produced from CT images . In comparison , state-of-the art adversarial networks trained without our tumor-aware loss produced MRIs with ill-preserved or missing tumors .
The key issue in unsupervised visual domain adaptation is how to do the feature alignment between source and target domains . Inspired by the adversarial learning in generative adversarial networks , this study proposes a novel adversarial auto-encoder for unsupervised deep domain adaptation . This method incorporates the auto-encoder with the adversarial learning so that the domain similarity and reconstruction information from the decoder can be exploited to facilitate the adversarial domain adaptation in the encoder .
The procedure of tackling this discrepancy between the training ( source ) and testing ( target ) domains is known as domain adaptation . We propose an unsupervised version of domain adaptation that considers the presence of only unlabelled data in the target domain . Our approach centres on finding correspondences between samples of each domain .
We showed that via enforcing a simple weight - sharing constraint to the layers that are responsible for decoding abstract semantics , the CoGAN learned the joint distribution of images by just using samples drawn separately from the marginal distributions . In addition to convincing image generation results on faces and RGBD images , we also showed promising results of the CoGAN framework for the image transformation and unsupervised domain adaptation tasks . bibliography : References
As far as we know , the domain transfer problem we formulate is novel despite being ecological ( i.e. , appearing naturally in the real - world ) , widely applicable , and related to cognitive reasoning thewaywethink . In the discussion below , we survey recent GAN work , compare our work to the recent image synthesis work and make links to unsupervised domain adaptation . GAN gan methods train a generator network that synthesizes samples from a target distribution given noise vectors .
Their method is able to invert the mid - level activations of AlexNet and reconstruct the input image . In contrast , we solve the problem of unsupervised domain transfer and apply the loss terms in different domains : pixel loss in the target domain , and feature loss in the source domain . Another class of very promising generative techniques that has recently gained traction is neural style transfer .
Unlike those works and similarly to e.g. DLID we use the whole unlabeled target domain ( as the premise of our method is the abundance of unlabeled data in the target domain ) . Under this transductive setting , our method is able to improve previously - reported state - of - the - art accuracy for unsupervised adaptation very considerably ( Table [ reference ] ) , especially in the most challenging Amazon Webcam scenario ( the two domains with the largest domain shift ) . section : Discussion
CoGANs liu_arxiv16 jointly learn a source and target representation through explicit weight sharing of certain layers while each source and target has a unique generative adversarial objective . ghifary_eccv16 uses an additional reconstruction objective in the target domain to encourage alignment in the unsupervised adaptation setting . In contrast , another approach is to directly convert the target image into a source style image ( or visa versa ) , largely based on Generative Adversarial Networks ( GANs ) goodfellow_nips14 .
section : Cycle - Consistent Adversarial Domain Adaption We consider the problem of unsupervised adaptation , where we are provided source data , source labels , and target data , but no target labels . The goal is to learn a model that can correctly predict the label for the target data .
Through this framework we are able to motivate a novel domain adaptation method , ADDA , and offer insight into our design decisions . In the next section we demonstrate promising results on unsupervised adaptation benchmark tasks , studying adaptation across digits and across modalities . section : Experiments We now evaluate ADDA for unsupervised classification adaptation across four different domain shifts . We explore three digits datasets of varying difficulty : MNIST , USPS , and SVHN .
subsection : MNIST , USPS , and SVHN digits datasets We experimentally validate our proposed method in an unsupervised adaptation task between the MNIST , USPS , and SVHN digits datasets , which consist 10 classes of digits . Example images from each dataset are visualized in Figure [ reference ] and Table [ reference ] .
In contrast , our framework applies to an unsupervised scenario where an obvious partition of the dataset is unknown . Furthermore , extending GMAN to the semi - supervised domain - adaptation scenario would suggest multiple discriminators per domain , therefore our line of research is strictly orthogonal to that of their multi - domain discriminator approach . Also , note that assigning a discriminator to each domain is akin to prescribing a new discriminator to each value of a conditional variable in conditional GANs [ reference ] ) .
We consider the development set of ANSWERS as our only target dev set to set hyperparameters . This may result in suboptimal per - domain settings but better resembles an unsupervised adaptation scenario . section : Sentiment analysis
Ideally , we wish to have a trained ( locked ) model that can work uniformly well across unseen domains without further training . In this paper , we propose a deep stacked transformation approach for domain generalization . Specifically , a series of $ {n}$ stacked transformations are applied to each image during network training .
Domain generalization ( DG ) aims to incorporate knowledge from multiple source domains into a single model that could generalize well on unseen target domains . This problem is ubiquitous in practice since the distributions of the target data may rarely be identical to those of the source data .
This limits the practical applicability of FG-SBIR . In this paper , we identify cross-category generalisation for FG-SBIR as a domain generalisation problem , and propose the first solution . Our key contribution is a novel unsupervised learning approach to model a universal manifold of prototypical visual sketch traits .
If DA methods are applied directly to DG by a simple exclusion of the target data from training , poor performance will result for a given task . In this paper , we tackle the domain generalization challenge in two ways . In our first approach , we propose a novel deep domain generalization architecture utilizing synthetic data generated by a Generative Adversarial Network ( GAN ) .
Experiments about the weakly-supervised semantic segmentation task show the effectiveness of our method . Besides , we further explore and validate that the proposed method is able to improve the generalization ability of a classification network in domain adaptation and domain generalization settings .
Our results show that even the state-of-the-art deep learning models show significant performance degradation in the presence of ambient acoustic noise , and more surprisingly under scenarios of microphone variability , with accuracy losses as high as 15 % in some scenarios . Further , we provide intuition on how this problem of model robustness relates to the broader topic of dataset-shift in the machine learning literature , and highlight future research directions for the mobile sensing community which include the investigation of domain adaptation and domain generalization solutions in the context of sensing systems .
They are cooperatively trained with standard continuous and discrete optimization schemes . Our model does not require any linguistic structure for supervision and its recursive nature allows for out-of-domain generalization with little loss in performance . Additionally , our approach performs competitively on several natural language tasks , such as Natural Language Inference or Sentiment Analysis .
Finally , we introduce parameter sharing techniques to reduce the computation difficulty of our strategy . Empirical results show the superior performance of our proposed method on both toy dataset and well-used domain generalization tasks .
Partial domain adaptation aims to tackle this generalization issue where no labeled data are provided in target domain . This paper presents an adversarial learning for partial domain adaptation where a symmetric metric based on the Wasserstein distance is adopted in an adversarial learning objective . We build a Wasserstein partial transfer network where the Wasserstein adversarial objective is jointly optimized to partially transfer the relevance knowledge from source to target domains .
Enlarging the Domain Gap in Open Set Domain Adaptation . Unsupervised domain adaptation aims to leverage labeled data from a source domain to learn a classifier for an unlabeled target domain .
This approach was developed using a noisy TED Talks corpus and tested on a Wikipedia-based comparable corpus ; however , it can be extended to any text domain or language pair . The proposed method uses various statistical measures for sentence comparison and can be used for in-domain data adaptation tasks as well . Minimization of data loss was ensured by parameter adaptation .
We will study the adaptation of the recogniser as an input method for the post-editor , and investigate the improvement of speech transcription for translation purposes . Furthermore we will study how to perform automatic domain-adaptation for speech recognition , in order to automatically adapt the language models of the recogniser to the domain . • Workflows and personalised user interfaces – We aim at a higher comfort and productivity for the translators , by analysing and modelling current translation systems and translator 's workflows and practices , investigating new visualisations of translation features , and developing and testing new interfaces for translation work .
The paper presents scheme for doing Domain Adaptation for multiple domains simultaneously . The proposed method segments a large corpus into various parts using self-organizing maps ( SOMs ) .
G , and an indirect method with better extrapolation properties . We evaluate our methods on two crossdomain visual recognition benchmarks , where they perform close to the upper bound of full data domain adaptation . This demonstrates that data is not necessary for domain adaptation if a domain can be parametrically described .
In-domain adaptation ( DA ) , the knowledge trained in one domain , is used to test an unknown domain . Existing approaches use limited efforts on DA in sentiment classification ( SC ) using neural networks .
We show that under practical conditions , it defines a minimax game that can promote the joint distribution alignment . Except for the traditional closed set domain adaptation , we also extend DADA for extremely challenging problem settings of partial and open set domain adaptation . Experiments show the efficacy of our proposed methods and we achieve the new state of the art for all the three settings on benchmark datasets .
This paper introduces partial domain adaptation as a new domain adaptation scenario , which relaxes the fully shared label space assumption to that the source label space subsumes the target label space . Previous methods typically match the whole source domain to the target domain , which are vulnerable to negative transfer for the partial domain adaptation problem due to the large mismatch between label spaces . We present Partial Adversarial Domain Adaptation ( PADA ) , which simultaneously alleviates negative transfer by down-weighing the data of outlier source classes for training both source classifier and domain adversary , and promotes positive transfer by matching the feature distributions in the shared label space .
This degradation is due to the difference of probability distributions between training data and testing data , known as source data domain and target data domain , respectively . To better adapt a smart camera network to a specific situation , some form of target domain adaptation is needed , which usually requires both the source domain data and as much as possible target domain data at each node , which may strain storage capacity and bandwidth . In this paper , we propose an adaptation method which fuses the source specific hypotheses ( SSHs ) and target specific hypotheses ( TSHs ) - requiring only a pre-trained face detector and a few target data to be shared by peer-to-peer communications , thus relieving the storage and bandwidth constraints .
To overcome this limitation , we devise a general forward-backward splitting algorithm based on Bregman distances for solving a wide range of optimization problems involving a differentiable function with Lipschitz-continuous gradient and a doubly stochastic constraint . We illustrate the efficiency of our approach in the context of continuous domain adaptation . Experiments show that the proposed method leads to a significant improvement in terms of speed and performance with respect to the state of the art for domain adaptation on a continually rotating distribution coming from the standard two moon dataset .
In this work , we address the scenario in which the target domain is continually , albeit slowly , evolving , and in which , at different time frames , we are given a batch of test data to classify . We exploit the geometry-awareness that optimal transport offers for the resolution of continuous domain adaptation problems . We propose a regularized optimal transport model that takes into account the transportation cost , the entropy of the probabilistic coupling , the labels of the source domain , and the similarity between successive target domains .
Our contribution is the first deep architecture that tackles predictive domain adaptation , able to leverage over the information brought by the auxiliary domains through a graph . Moreover , we present a simple yet effective strategy that allows us to take advantage of the incoming target data at test time , in a continuous domain adaptation scenario . Experiments on three benchmark databases support the value of our approach .
Leveraging Crowdsourced GPS Data for Road Extraction From Aerial Imagery Prediction Reweighting for Domain Adaptation Transferring knowledge across robots: A risk sensitive approach AdaStereo: A Simple and Efficient Approach for Adaptive Stereo Matching Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application Modeling Skip-Grams for Event Detection with Convolutional Neural Networks Empirical Study of Domain Adaptation with Naïve Bayes on the Task of Splice Site Prediction Representation Learning for Improved Generalization of Adversarial Domain Adaptation with Text Classification Instance-based question answering Self-Training for Enhancement and Domain Adaptation of Statistical Parsers Trained on Small Datasets Domain adaptation in remote sensing through cross-image synthesis with dictionaries The University of Edinburgh’s systems submission to the MT task at IWSLT Unsupervised Visual Domain Adaptation Using Subspace Alignment Asymmetric Tri-training for Unsupervised Domain Adaptation ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes Domain-Adversarial Training of Neural Networks Revisiting Distributional Correspondence Indexing: A Python Reimplementation and New Experiments Strong Baselines for Neural Semi-supervised Learning under Domain Shift Unsupervised Domain Adaptation by Backpropagation Correlation Alignment for Unsupervised Domain Adaptation Unsupervised Domain Adaptation for Semantic Segmentation with Symmetric Adaptation Consistency A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation Transfer Domain Class Clustering for Unsupervised Domain Adaptation Deep Domain Adaptation by Geodesic Distance Minimization Unsupervised Visual Domain Adaptation Using Auxiliary Information in Target Domain Deep domain adaptation for anti-spoofing in speaker verification systems Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift Unsupervised Adversarial Domain Adaptation Based on The Wasserstein Distance For Acoustic Scene Classification Tumor-Aware, Adversarial Domain Adaptation from CT to MRI for Lung Cancer Segmentation Adversarial auto-encoder for unsupervised deep domain adaptation Sample-to-Sample Correspondence for Unsupervised Domain Adaptation Coupled Generative Adversarial Networks Unsupervised Cross-Domain Image Generation Unsupervised Domain Adaptation by Backpropagation CyCADA: Cycle-Consistent Adversarial Domain Adaptation Adversarial Discriminative Domain Adaptation Generative Multi-Adversarial Networks Strong Baselines for Neural Semi-supervised Learning under Domain Shift Generalizing Deep Learning for Medical Image Segmentation to Unseen Domains via Deep Stacked Transformation Domain Generalization via Multidomain Discriminant Analysis Generalising Fine-Grained Sketch-Based Image Retrieval Multi-Component Image Translation for Deep Domain Generalization Attention Bridging Network for Knowledge Transfer Towards the Design and Evaluation of Robust Audio-Sensing Systems Cooperative Learning of Disjoint Syntax and Semantics Invariant Feature Learning by Pairwise Neural Net Distance Partially Adversarial Learning and Adaptation Mind the Gap: Enlarging the Domain Gap in Open Set Domain Adaptation Statistical Approach to Noisy-Parallel and Comparable Corpora Filtering for the Extraction of Bi-lingual Equivalent Data at Sentence-Level Smart Computer Aided Translation Environment Extracting and Selecting Relevant Corpora for Domain Adaptation in MT Multivariate Regression on the Grassmannian for Predicting Novel Domains Hyperparameter tuning in convolutional neural networks for domain adaptation in sentiment classification (HTCNN-DASC) Discriminative Adversarial Domain Adaptation Partial Adversarial Domain Adaptation Target Domain Adaptation for Face Detection in a Smart Camera Network with Peer-to-Peer Communications Forward-Backward Splitting for Optimal Transport Based Problems CDOT: Continuous Domain Adaptation using Optimal Transport AdaGraph: Unifying Predictive and Continuous Domain Adaptation Through Graphs
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CVPR EMNLP BIOINFORMATICS ICIoT ACL IGARSS ICCV ICML 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2017 IEEE International Conference on Computer Vision (ICCV) J. Mach. Learn. Res. ArXiv ACL ICML Domain Adaptation in Computer Vision Applications ICASSP ISM MICCAI (2) NIPS ICLR ICML ICML 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICLR ACL UAI CVPR WACV ICCV Human Activity Sensing NAACL-HLT (1) EUSIPCO WorldCIST (1) ICON CVPR AAAI ECCV (8) GLOBECOM
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