An original assignment approach is also presented : based on dissimilarity measures this approach shows that in fact the structural behavior of the bridge seems totally different of the initial behaviors used for training the supervised learning methods . Supervised learning algorithms The basic idea is that locations that share similar properties of geo-spatial venue characteristics and human mobility tend to have closer popularity values . Experiments conducted on Foursquare check-in data in New York City exhibit promising results on predicting the temporal popularity of retail chains Starbucks , McDonald ’s , and Dunkin ’ Donuts , comparing to interpolation , regression , and state-of-the-art supervised learning methods . We introduce a novel self-supervised learning method based on adversarial training . Our objective is to train a discriminator network to distinguish real images from images with synthetic artifacts , and then to extract features from its intermediate layers that can be transferred to other data domains and tasks . Obstacle detection by monocular vision is challenging because a single camera does not provide a direct measure for absolute distances to objects . A self-supervised learning approach is proposed that combines a camera and a very small short-range proximity sensor to find the relation between the appearance of objects in camera images and their corresponding distances . The method is efficient enough to run real time on a small camera system that can be carried onboard a lightweight MAV of 19 g. However , these traditional methods spend too much time extracting features , reducing feature dimension and fusing different features . To resolve the issue , this paper presents an effective unsupervised self-learning method to achieve the fault diagnosis of centrifugal pumps , which uses deep learning method to adaptively extract fault features from non-stationary vibration signals and softmax regression model is used to identify possible failure modes automatically . In particular , the stacked denoising autoencoder ( SDA ) of deep learning models is selected to learn effective feature representations and we improved fault pattern classification robustness by corrupting the input data . The experiments showed that our best performing model significantly outperforms the BM25 model ( our weak supervision signal ) by over and MAP improvements in the Robust04 and ClueWeb collections , respectively . We also demonstrated that in the case of having a small amount of training data , we can improve the performance of supervised learning by pre - training the network on weakly supervised data . Based on our results , there are three key ingredients in neural ranking models that lead to good performance with weak supervision : Li incorporate model joint dependencies in the CNN via a max - margin formalism , others impose kinematic constraints by embedding a differentiable kinematic model into the deep learning architecture . Tekin propose a deep regression architecture for structured prediction that combines traditional CNNs for supervised learning with an auto - encoder that implicitly encodes 3D dependencies between body parts . As CNNs have become more prevalent , 2D joint estimation has become increasingly reliable and many recent works have looked to exploit this using a pipeline approach . Based on the spatial correlations among subapertures , the plenoptic video is divided into two subaperture groups : the central view videos and the residual videos . Coding methods , multiview coding method based on spatial correlation(S-MVC ) and multiview coding method based on temporal correlation(T-MVC ) , are adopted for residual videos according to correlation and residual energy analysis , while the central view videos are always encoded by S-MVC . Correlation analysis is performed first , and residual energy analysis is further performed if necessary . Complexity of multiview coding is proportional to the number of cameras . It makes difficult to implement multiview sequences in real applications . With the end-to-end distortion model , the server can select the optimal encoding combination for transmission . Experimental results demonstrate that the proposed method can achieve a significant end-to-end RD performance improvement over the selective streaming methods with simulcast coding or scalable multiview coding . In addition , it has better error-resilience performance to combat with packet-losses over the Internet protocol ( IP ) networks . The overall quality is better than regular AVC/H.264 . For compatibility with the framework of scalable multiview coding , we introduce depth scalability . This ensures that the ( hierarchical layerwise ) encoded video bitstream can be optimally displayed on various displays with different depth properties . This work presents a new distributed multiview coding framework , based on the H.264/AVC standard operating with mixed resolution frames . It allows for a scalable complexity transfer from the encoder to the decoder , which is particularly suited for low-power video applications , such as multiview surveillance systems . To guarantee the best bitrate performance , our algorithm adaptively re-sorts the reference frame list , such that minimum number of bits is used for coding reference frame indices . Performance evaluations show that our proposed coding method outperforms the recent multiview coding standard by up to 1 dB PSNR and enhances the compression ratio by 22.97 % . In [ 1,2 ] we presented a multiview image coding scheme . The approach is based on extracting depth layers from multiview images . Many existing multiview image/video coding techniques remove inter-viewpoint redundancy by applying disparity compensation in a conventional video coding framework , e.g. , H.264/MPEG-4 AVC . However , conventional methodology works ineffectively as it ignores the special characteristics of inter-viewpoint disparity . In this paper , we propose a geometric prediction methodology for accurate disparity vector ( DV ) prediction , such that we can largely reduce the disparity compensation cost . Based on the new DV predictor , we design a basic framework that can be implemented in most existing multiview image/video coding schemes . We also use state-of-the-art H.264/MPEG-4 AVC as an example to illustrate how the proposed framework can be integrated with conventional video coding algorithms . Each node in this system includes a camera to capture with a limited processing and communication abilities . We propose a multiview images coding without requiring inter-node communication , to gain the advantage of correlation at decoder side for two network configurations , but the main concept of the proposed coding for both network configurations is the same . The two network configurations are distinguished based on presence of parent node in each cluster , which sends full information to central node ( i.e. joint decoder ) . View interpolation has been applied in multiview view coding . However , existing schemes assume all cameras are aligned . In this paper , a perceptual multiview video coding scheme is proposed , based on the synthesized Just Noticeable Distortion ( JND ) maps . In JND-based perceptual video coding , the residues after intra or inter prediction are tuned according to the corresponding JND thresholds to save the bits without affecting the perceptual quality . Moreover , the experiments clearly show that the performance of the BiGAN has a direct correlation with the dimensions of the dataset used for training . The BiGAN even tends to outperform the well-established semi-supervised One-class SVM classifier and a prominent generative network for semi-supervised anomaly detection , Variational Autoencoders ( VAEs ) when the size of the feature space increases . We consider an extension of this framework in two directions . First , based on the BiGAN model , the Multi-view BiGAN ( MV-BiGAN ) is able to perform density estimation from multi-view inputs . Second , it can deal with missing views and is able to update its prediction when additional views are provided . In this work we introduce a novel approach to train Bidirectional Generative Adversarial Model ( BiGAN ) in a semi-supervised manner . The presented method utilizes triplet loss function as an additional component of the objective function used to train discriminative data representation in the latent space of the BiGAN model . This representation can be further used as a seed for generating artificial images , but also as a good feature embedding for classification and image retrieval tasks . Because LIBOR is so important , decided by so many huge banks together and impacted by both current demand and supply of monetary currency and the forecast of future market , therefore the prediction is quite challenging . This paper is to introduce genetic algorithm ( “ GA ” ) based bi-directional generative adversary network ( “ BiGAN ” ) to predict the LIBOR in USD . Both the pro and cons of the algorithm will be discussed , with fitness values and Mean Squared Error ( “ MSE ” ) . Conditional variants of Generative Adversarial Networks ( GANs ) , known as cGANs , are generative models that can produce data samples ( $ x$ ) conditioned on both latent variables ( $ z$ ) and known auxiliary information ( $ c$ ) . Another GAN variant , Bidirectional GAN ( BiGAN ) is a recently developed framework for learning the inverse mapping from $ x$ to $ z$ through an encoder trained simultaneously with the generator and the discriminator of an unconditional GAN . We propose the Bidirectional Conditional GAN ( BCGAN ) , which combines cGANs and BiGANs into a single framework with an encoder that learns inverse mappings from $ x$ to both $ z$ and $ c$ , trained simultaneously with the conditional generator and discriminator in an end-to-end setting . Generative Adversarial Networks ( GAN ) , which have excellent performance in modelling the complex distributions of real-world data , shows its effectiveness in anomaly detection . In this paper , the application of Bidirectional Generative Adversarial Network ( BiGAN ) model for anomaly detection has been introduced and investigated . Besides , scoring functions has also been applied to estimate the anomaly of new data samples . GANs have been applied to generate natural images of objects , such as digits and faces , and have been extended in several ways . The BiGAN approach extends GANs to also learn the inverse mapping from the image data back into the latent space , and shows that this can learn features useful for image classification tasks . The conditional generative adversarial net ( CGAN ) is an extension of the GAN where both networks G and D receive an additional vector of information as input . Adversarial variational Bayes ference , avb uses a more general amortized GAN inference framework within a maximum - likelihood learning setting . Another type of GAN inference technique is used in the ALI ali and BiGAN bigan models , which have been shown to approximate maximum likelihood learning ference . In these models , both the recognition and generative models are implicit and are jointly learnt by an adversarial training process . Conditional variants of Generative Adversarial Networks ( GANs ) , known as cGANs , are generative models that can produce data samples ( $ x$ ) conditioned on both latent variables ( $ z$ ) and known auxiliary information ( $ c$ ) . Another GAN variant , Bidirectional GAN ( BiGAN ) is a recently developed framework for learning the inverse mapping from $ x$ to $ z$ through an encoder trained simultaneously with the generator and the discriminator of an unconditional GAN . We propose the Bidirectional Conditional GAN ( BCGAN ) , which combines cGANs and BiGANs into a single framework with an encoder that learns inverse mappings from $ x$ to both $ z$ and $ c$ , trained simultaneously with the conditional generator and discriminator in an end-to-end setting . In particular , in the first stage , we propose a shape enhanced clothing deformation model for deforming the clothing item , where the user body shape is incorporated as the intermediate guidance . For the second stage , we present an attentive bidirectional GAN , which jointly models the attentive clothing-person alignment and bidirectional generation consistency . For evaluation , we create a large-scale dataset , FashionTryOn , comprising $ 28,714 $ triplets with each consisting of a clothing item image and two model images in different poses . We evaluate models based on their ability to produce high quality samples and reconstructions of real images . Our main contributions are twofold : First , we find that the baseline Bidirectional GAN ( BiGAN ) can be improved upon with the addition of an autoencoder loss , at the expense of an extra hyper-parameter to tune . Second , we show that comparable performance to BiGAN can be obtained by simply training an encoder to invert the generator of a normal GAN . Unlike the Structured GAN our training procedure is completely unsupervised , can detect unknown data - generating factors , and only introduces one additional inference network ( the encoder ) . In contrast to the Bidirectional GAN we replace the simple prior on the latent representation with a distribution that is amenable to disentangled representations and introduce an additional loss for the encoder and the generator to achieve disentangled representations . On the MNIST , CelebA , and SVHN data sets we show that the encoder does learn interpretable representations which encode meaningful properties of the data distribution . We feed G to a 128 - dimensional bidirectional GRU to obtain the memory M = bidirectional - GRU ( G ) , where M ∈ R 256×T . Internal State Controller :
An original assignment approach is also presented : based on dissimilarity measures this approach shows that in fact the structural behavior of the bridge seems totally different of the initial behaviors used for training the supervised learning methods . Supervised learning algorithms
The basic idea is that locations that share similar properties of geo-spatial venue characteristics and human mobility tend to have closer popularity values . Experiments conducted on Foursquare check-in data in New York City exhibit promising results on predicting the temporal popularity of retail chains Starbucks , McDonald ’s , and Dunkin ’ Donuts , comparing to interpolation , regression , and state-of-the-art supervised learning methods .
We introduce a novel self-supervised learning method based on adversarial training . Our objective is to train a discriminator network to distinguish real images from images with synthetic artifacts , and then to extract features from its intermediate layers that can be transferred to other data domains and tasks .
Obstacle detection by monocular vision is challenging because a single camera does not provide a direct measure for absolute distances to objects . A self-supervised learning approach is proposed that combines a camera and a very small short-range proximity sensor to find the relation between the appearance of objects in camera images and their corresponding distances . The method is efficient enough to run real time on a small camera system that can be carried onboard a lightweight MAV of 19 g.
However , these traditional methods spend too much time extracting features , reducing feature dimension and fusing different features . To resolve the issue , this paper presents an effective unsupervised self-learning method to achieve the fault diagnosis of centrifugal pumps , which uses deep learning method to adaptively extract fault features from non-stationary vibration signals and softmax regression model is used to identify possible failure modes automatically . In particular , the stacked denoising autoencoder ( SDA ) of deep learning models is selected to learn effective feature representations and we improved fault pattern classification robustness by corrupting the input data .
The experiments showed that our best performing model significantly outperforms the BM25 model ( our weak supervision signal ) by over and MAP improvements in the Robust04 and ClueWeb collections , respectively . We also demonstrated that in the case of having a small amount of training data , we can improve the performance of supervised learning by pre - training the network on weakly supervised data . Based on our results , there are three key ingredients in neural ranking models that lead to good performance with weak supervision :
Li incorporate model joint dependencies in the CNN via a max - margin formalism , others impose kinematic constraints by embedding a differentiable kinematic model into the deep learning architecture . Tekin propose a deep regression architecture for structured prediction that combines traditional CNNs for supervised learning with an auto - encoder that implicitly encodes 3D dependencies between body parts . As CNNs have become more prevalent , 2D joint estimation has become increasingly reliable and many recent works have looked to exploit this using a pipeline approach .
Based on the spatial correlations among subapertures , the plenoptic video is divided into two subaperture groups : the central view videos and the residual videos . Coding methods , multiview coding method based on spatial correlation(S-MVC ) and multiview coding method based on temporal correlation(T-MVC ) , are adopted for residual videos according to correlation and residual energy analysis , while the central view videos are always encoded by S-MVC . Correlation analysis is performed first , and residual energy analysis is further performed if necessary .
Complexity of multiview coding is proportional to the number of cameras . It makes difficult to implement multiview sequences in real applications .
With the end-to-end distortion model , the server can select the optimal encoding combination for transmission . Experimental results demonstrate that the proposed method can achieve a significant end-to-end RD performance improvement over the selective streaming methods with simulcast coding or scalable multiview coding . In addition , it has better error-resilience performance to combat with packet-losses over the Internet protocol ( IP ) networks .
The overall quality is better than regular AVC/H.264 . For compatibility with the framework of scalable multiview coding , we introduce depth scalability . This ensures that the ( hierarchical layerwise ) encoded video bitstream can be optimally displayed on various displays with different depth properties .
This work presents a new distributed multiview coding framework , based on the H.264/AVC standard operating with mixed resolution frames . It allows for a scalable complexity transfer from the encoder to the decoder , which is particularly suited for low-power video applications , such as multiview surveillance systems .
To guarantee the best bitrate performance , our algorithm adaptively re-sorts the reference frame list , such that minimum number of bits is used for coding reference frame indices . Performance evaluations show that our proposed coding method outperforms the recent multiview coding standard by up to 1 dB PSNR and enhances the compression ratio by 22.97 % .
In [ 1,2 ] we presented a multiview image coding scheme . The approach is based on extracting depth layers from multiview images .
Many existing multiview image/video coding techniques remove inter-viewpoint redundancy by applying disparity compensation in a conventional video coding framework , e.g. , H.264/MPEG-4 AVC . However , conventional methodology works ineffectively as it ignores the special characteristics of inter-viewpoint disparity . In this paper , we propose a geometric prediction methodology for accurate disparity vector ( DV ) prediction , such that we can largely reduce the disparity compensation cost . Based on the new DV predictor , we design a basic framework that can be implemented in most existing multiview image/video coding schemes . We also use state-of-the-art H.264/MPEG-4 AVC as an example to illustrate how the proposed framework can be integrated with conventional video coding algorithms .
Each node in this system includes a camera to capture with a limited processing and communication abilities . We propose a multiview images coding without requiring inter-node communication , to gain the advantage of correlation at decoder side for two network configurations , but the main concept of the proposed coding for both network configurations is the same . The two network configurations are distinguished based on presence of parent node in each cluster , which sends full information to central node ( i.e. joint decoder ) .
View interpolation has been applied in multiview view coding . However , existing schemes assume all cameras are aligned .
In this paper , a perceptual multiview video coding scheme is proposed , based on the synthesized Just Noticeable Distortion ( JND ) maps . In JND-based perceptual video coding , the residues after intra or inter prediction are tuned according to the corresponding JND thresholds to save the bits without affecting the perceptual quality .
Moreover , the experiments clearly show that the performance of the BiGAN has a direct correlation with the dimensions of the dataset used for training . The BiGAN even tends to outperform the well-established semi-supervised One-class SVM classifier and a prominent generative network for semi-supervised anomaly detection , Variational Autoencoders ( VAEs ) when the size of the feature space increases .
We consider an extension of this framework in two directions . First , based on the BiGAN model , the Multi-view BiGAN ( MV-BiGAN ) is able to perform density estimation from multi-view inputs . Second , it can deal with missing views and is able to update its prediction when additional views are provided .
In this work we introduce a novel approach to train Bidirectional Generative Adversarial Model ( BiGAN ) in a semi-supervised manner . The presented method utilizes triplet loss function as an additional component of the objective function used to train discriminative data representation in the latent space of the BiGAN model . This representation can be further used as a seed for generating artificial images , but also as a good feature embedding for classification and image retrieval tasks .
Because LIBOR is so important , decided by so many huge banks together and impacted by both current demand and supply of monetary currency and the forecast of future market , therefore the prediction is quite challenging . This paper is to introduce genetic algorithm ( “ GA ” ) based bi-directional generative adversary network ( “ BiGAN ” ) to predict the LIBOR in USD . Both the pro and cons of the algorithm will be discussed , with fitness values and Mean Squared Error ( “ MSE ” ) .
Conditional variants of Generative Adversarial Networks ( GANs ) , known as cGANs , are generative models that can produce data samples ( $ x$ ) conditioned on both latent variables ( $ z$ ) and known auxiliary information ( $ c$ ) . Another GAN variant , Bidirectional GAN ( BiGAN ) is a recently developed framework for learning the inverse mapping from $ x$ to $ z$ through an encoder trained simultaneously with the generator and the discriminator of an unconditional GAN . We propose the Bidirectional Conditional GAN ( BCGAN ) , which combines cGANs and BiGANs into a single framework with an encoder that learns inverse mappings from $ x$ to both $ z$ and $ c$ , trained simultaneously with the conditional generator and discriminator in an end-to-end setting .
Generative Adversarial Networks ( GAN ) , which have excellent performance in modelling the complex distributions of real-world data , shows its effectiveness in anomaly detection . In this paper , the application of Bidirectional Generative Adversarial Network ( BiGAN ) model for anomaly detection has been introduced and investigated . Besides , scoring functions has also been applied to estimate the anomaly of new data samples .
GANs have been applied to generate natural images of objects , such as digits and faces , and have been extended in several ways . The BiGAN approach extends GANs to also learn the inverse mapping from the image data back into the latent space , and shows that this can learn features useful for image classification tasks . The conditional generative adversarial net ( CGAN ) is an extension of the GAN where both networks G and D receive an additional vector of information as input .
Adversarial variational Bayes ference , avb uses a more general amortized GAN inference framework within a maximum - likelihood learning setting . Another type of GAN inference technique is used in the ALI ali and BiGAN bigan models , which have been shown to approximate maximum likelihood learning ference . In these models , both the recognition and generative models are implicit and are jointly learnt by an adversarial training process .
Conditional variants of Generative Adversarial Networks ( GANs ) , known as cGANs , are generative models that can produce data samples ( $ x$ ) conditioned on both latent variables ( $ z$ ) and known auxiliary information ( $ c$ ) . Another GAN variant , Bidirectional GAN ( BiGAN ) is a recently developed framework for learning the inverse mapping from $ x$ to $ z$ through an encoder trained simultaneously with the generator and the discriminator of an unconditional GAN . We propose the Bidirectional Conditional GAN ( BCGAN ) , which combines cGANs and BiGANs into a single framework with an encoder that learns inverse mappings from $ x$ to both $ z$ and $ c$ , trained simultaneously with the conditional generator and discriminator in an end-to-end setting .
In particular , in the first stage , we propose a shape enhanced clothing deformation model for deforming the clothing item , where the user body shape is incorporated as the intermediate guidance . For the second stage , we present an attentive bidirectional GAN , which jointly models the attentive clothing-person alignment and bidirectional generation consistency . For evaluation , we create a large-scale dataset , FashionTryOn , comprising $ 28,714 $ triplets with each consisting of a clothing item image and two model images in different poses .
We evaluate models based on their ability to produce high quality samples and reconstructions of real images . Our main contributions are twofold : First , we find that the baseline Bidirectional GAN ( BiGAN ) can be improved upon with the addition of an autoencoder loss , at the expense of an extra hyper-parameter to tune . Second , we show that comparable performance to BiGAN can be obtained by simply training an encoder to invert the generator of a normal GAN .
Unlike the Structured GAN our training procedure is completely unsupervised , can detect unknown data - generating factors , and only introduces one additional inference network ( the encoder ) . In contrast to the Bidirectional GAN we replace the simple prior on the latent representation with a distribution that is amenable to disentangled representations and introduce an additional loss for the encoder and the generator to achieve disentangled representations . On the MNIST , CelebA , and SVHN data sets we show that the encoder does learn interpretable representations which encode meaningful properties of the data distribution .
We feed G to a 128 - dimensional bidirectional GRU to obtain the memory M = bidirectional - GRU ( G ) , where M ∈ R 256×T . Internal State Controller :
Supervised learning algorithms for damage detection and long term bridge monitoring Temporal popularity prediction of locations for geographical placement of retail stores Self-Supervised Feature Learning by Learning to Spot Artifacts Self-supervised monocular distance learning on a lightweight micro air vehicle Fault diagnosis for centrifugal pumps using deep learning and softmax regression Neural Ranking Models with Weak Supervision Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image Efficient Content Adaptive Plenoptic Video Coding Fast Mode Decision Algorithm for Depth Coding in 3D Video Systems Using H.264/AVC RD-optimized interactive streaming of multiview video with multiple encodings Display dependent coding for 3D video on automultiscopic displays Mixed resolution framework for distributed multiview coding A new prediction structure for multiview video coding A fast layer-based multiview image coding algorithm Multiview Image Coding Based on Geometric Prediction Distributed source coding of multiview images Rate-distortion analysis of rectification-based view interpolation for multiview video coding Perceptual multiview video coding using synthesized Just Noticeable Distortion maps Generative Adversarial Networks (GAN) based Anomaly Detection in Industrial Software Systems Multi-view Generative Adversarial Networks Semi-supervised learning with Bidirectional GANs Genetic Algorithm Based Bi-directional Generative Adversary Network for LIBOR Prediction Bidirectional Conditional Generative Adversarial Networks Generative Adversarial Network Based Anomaly Detection on the Benchmark Tennessee Eastman Process Adversarial Discriminative Domain Adaptation PixelGAN Autoencoders Bidirectional Conditional Generative Adversarial Networks Virtually Trying on New Clothing with Arbitrary Poses An Empirical Study of Generative Models with Encoders Inferencing based on unsupervised learning of disentangled representations ReasoNet: Learning to Stop Reading in Machine Comprehension
3aeec58676507f3f9ebb5946a2f5e098cd07dd17 1a28b3914ae83b05ec36ab45496914b57c29917b 5b2193c44f2925807c5978a24da3381324969c40 99a235381af9ecf6b6621ff2ae12824ff764a818 cffbd485e641e01f0f32042845f32e68b7fac4d3 3fca9024695bb400caefec4d9ca328b145f0bc1c 81b6059e4f18b4c40de40349574ef72d49258ab9 78b2f29a413fd0ea6c34a5e75639b20e4a073d49 478464f0dc4ce5a2e41db9c16a7799a22130ac02 08475b41265684b714fa4cbf576ace62932864d1 0c684a92a0c37deb0ce4da7ef10f94fd324dfcc5 b89056c58a1fb10703983645ad302e50c8ef4a1a b643290a2d3aa76522f73955e3384fd45746aea5 d23ff5d68aace3298a410148742c1f87b5b47d8f 1b4c2cc6c1e8e3609f6c8d0e8a3586efb16e4783 2a6c703c0f429a8f39ae013be8ed4a3ba1f751ca 1afc2b919d17d20ad5b0ffdf1a77d7df44f976f4 85b047b3534f55b81ccf37d11abc59f7ba597845 2c9d0795403e61fddda0b3cc4be145d0b24064a2 123494808a3a879d6ba86bd82857a3b9891ea603 0a02184707247a79a236a03f85ab35d99a68be33 d0720adad768f022c10825890e63e297ee4830a5 0a02184707247a79a236a03f85ab35d99a68be33 d488b9a7f019e518dfd2ad083fc657381c88f73c 9f5a57a9ea6557ac3deda4fe25b6bd9c914cf3be
["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Physics","Engineering"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Engineering","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Mathematics","Computer Science"] ["Engineering"] ["Mathematics","Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"]
2012 2019 2018 2016 2016 2017 2017 2020 2011 2010 2008 2010 2009 2012 2007 2004 2009 2011 2019 2017 2019 2020 2017 2019 2017 2017 2017 2019 2018 2018 2016
CVPR IROS SIGIR 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) PSIVT (2) ICIP EUSIPCO VCIP ICME ISCAS ECML/PKDD (2) ACIIDS (1) EIDWT 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) NIPS ACM Multimedia ESANN CoCo@NIPS
https://api.semanticscholar.org/3aeec58676507f3f9ebb5946a2f5e098cd07dd17 https://api.semanticscholar.org/1a28b3914ae83b05ec36ab45496914b57c29917b https://api.semanticscholar.org/5b2193c44f2925807c5978a24da3381324969c40 https://api.semanticscholar.org/99a235381af9ecf6b6621ff2ae12824ff764a818 https://api.semanticscholar.org/cffbd485e641e01f0f32042845f32e68b7fac4d3 https://www.semanticscholar.org/paper/c7a59fc69aee480e53cd1f869bad4c83170d1328 https://www.semanticscholar.org/paper/4be92654f72410d20d3a6a57a5c434b619d8605e https://api.semanticscholar.org/3fca9024695bb400caefec4d9ca328b145f0bc1c https://api.semanticscholar.org/81b6059e4f18b4c40de40349574ef72d49258ab9 https://api.semanticscholar.org/78b2f29a413fd0ea6c34a5e75639b20e4a073d49 https://api.semanticscholar.org/478464f0dc4ce5a2e41db9c16a7799a22130ac02 https://api.semanticscholar.org/08475b41265684b714fa4cbf576ace62932864d1 https://api.semanticscholar.org/0c684a92a0c37deb0ce4da7ef10f94fd324dfcc5 https://api.semanticscholar.org/b89056c58a1fb10703983645ad302e50c8ef4a1a https://api.semanticscholar.org/b643290a2d3aa76522f73955e3384fd45746aea5 https://api.semanticscholar.org/d23ff5d68aace3298a410148742c1f87b5b47d8f https://api.semanticscholar.org/1b4c2cc6c1e8e3609f6c8d0e8a3586efb16e4783 https://api.semanticscholar.org/2a6c703c0f429a8f39ae013be8ed4a3ba1f751ca https://api.semanticscholar.org/1afc2b919d17d20ad5b0ffdf1a77d7df44f976f4 https://api.semanticscholar.org/85b047b3534f55b81ccf37d11abc59f7ba597845 https://api.semanticscholar.org/2c9d0795403e61fddda0b3cc4be145d0b24064a2 https://api.semanticscholar.org/123494808a3a879d6ba86bd82857a3b9891ea603 https://api.semanticscholar.org/0a02184707247a79a236a03f85ab35d99a68be33 https://api.semanticscholar.org/d0720adad768f022c10825890e63e297ee4830a5 https://www.semanticscholar.org/paper/345afa0e85cb2f5cb438ae44027499ff2c392409 https://www.semanticscholar.org/paper/1130d8fdd931225c2d7563c3808367726cfa1c3a https://api.semanticscholar.org/0a02184707247a79a236a03f85ab35d99a68be33 https://api.semanticscholar.org/d488b9a7f019e518dfd2ad083fc657381c88f73c https://api.semanticscholar.org/9f5a57a9ea6557ac3deda4fe25b6bd9c914cf3be https://www.semanticscholar.org/paper/148eb2b649ce0f66214fceba5c061b417ab88d79 https://www.semanticscholar.org/paper/c636a2dd242908fe2e598a1077c0c57bfdea8633