As a reaction to their changing environment , many contemporary artists are using the newly available tools of three of today ’s most important technologies — video , computers and communications technology — in their search for new forms of creative expression , video art has evolved from artists ’ use of the tools of television for their own creative ends . Computers offer unlimited possibilities for image generation and the manipulation of vast quantities of information in the creative process . Communication technology allows interactivity in both these fields as well as the creation of new work spaces that eliminate distances between collaborators . The counterpart to a generative model is its statistical inverse , called a recognition model ( Hinton & Zemel , 1994 ) . This takes low-level activities and produces probability distributions over the entities in the world that could have led to them , expressed as activities of neurons in higher visual areas that model the image generation process . Even if a generative model is computationally tractable , its associated recognition model may not be . The invention discloses a somatosensory interactive system . The somatosensory interactive system comprises a video collecting device which is used for capturing dance actions of a user , the video collecting device is connected with a video collecting card which is used for transforming dance action analog signals shot by the video collecting device to digital signals , the video collecting card is connected with an action processing module which is used for compressing and encoding the digital signals output by the video collecting card into digital videos and recording the beautiful dance postures after analytically processing dance postures , the action processing module is connected with a main control server , the main control server is connected with an image generating module , the image generating module is connected with a projector , and a projection screen supporting the use of the projector display contrast images to a user . According to the somatosensory interactive system , by recording the beautiful dance postures , dance postures captured in real time are compared with the beautiful dance postures recorded before , the contrast result is presented to the user in a graph form , and the user is helped to correct the dance postures in real time . For a given image generation problem , the intrinsic image manifold is often low dimensional . We use the intuition that it is much better to train the GAN generator by minimizing the distributional distance between real and generated images in a small dimensional feature space representing such a manifold than on the original pixel-space . An image generation device ( 100 ) generates an output image on the basis of an input image captured by a camera ( 2 ) attached to a shovel ( 60 ) . The image generation device ( 100 ) is equipped with an output image generation means ( 11 ) that generates an output image by associating the values for input pixels in the input image with the values for output pixels in the output image . The value of an output pixel specified with integer coordinate points in an output image plane is determined on the basis of the values of two input pixels , which are specified by two integer coordinate points ( B , D ) selected from four integer coordinate points ( A-D ) located around a real number coordinate point ( P ) in an input image plane corresponding to those integer coordinate points of the output pixel . In 2000 , a new system was developed , built upon a Java prototype of a quality control graphical client interface for wind observations and an object-relational database . The objective was to integrate them in a distributed object approach with the legacy code responsible for the actual real-time wind analysis and image product generation . Common Object Request Broker Architecture ( CORBA ) was evaluated , but Java Remote Method Invocation ( AMI ) offered important advantages in terms of reuse and deployment . A method according to any of claims 1 - 2 , wherein the collecting protocol includes one of a set of system protocols for collecting ( 100 ) the image forming subekta.4 selected for scanning . A method according to any of claims 1 - 2 , further comprising : generating two or more cards dose for two or more protocols collection system ( 100 ) of the image forming generation , at least one of the two or more sound cards or two or more cards relationship contrast / noise ratio for two or more collection protocols ; iodnovremennoe dose map display and , at least one of the noise maps or maps relationships contrast / shum.5 . A method according to any of claims 1 - 2 , further comprising : receiving input indicative of a change at least one parameter value acquisition protocol , and generation and display of the dose maps and the noise maps on the basis izmeneniya.6 . In this paper , we employ Convolutional Neural Network method to design a classifier , by which gram staining images can be identified as normal group and disease model group effectively and correctly . And image generate method is used to improve classification accuracy . The fecal gram stain smears of health rats and irritable bowel syndrome model rats are employed to validate the proposed model . 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 . Researchers have successfully applied GANs to various applications such as image generation denton2015deep , radford2015unsupervised , zhao2016energy , image editing zhu2016generative and feature learning salimans2016improved , donahue2016adversarial . Recent work isola2016image , sangkloy2016scribbler , karacan2016learning adopt conditional GANs mirza_arxiv14 for these image - to - image translation problems isola2016image , but they require input - output image pairs for training , which is in general not available in domain adaptation problems . [ reference ] . The SAN consists of three major components : ( 1 ) the image model , which uses a CNN to extract high level image representations , e.g. one vector for each region of the image ; ( 2 ) the question model , which uses a CNN or a LSTM to extract a semantic vector of the question and ( 3 ) the stacked attention model , which locates , via multi - step reasoning , the image regions that are relevant to the question for answer prediction . As illustrated in Fig . The non - parametric models often do matching from a database of existing images , often matching patches of images , and have been used in texture synthesis efros1999texture , super - resolution freeman2002example and in - painting hays2007scene . Parametric models for generating images has been explored extensively ( for example on MNIST digits or for texture synthesis portilla2000parametric ) . However , generating natural images of the real world have had not much success until recently . [ reference ] synthesize 128×128 images of birds and people conditioned on text descriptions and on spatial constraints such as bounding boxes or keypoints . Wang and Gupta [ reference ] synthesize 128×128 images of indoor scenes by factorizing the image generation process into synthesis of a normal map and subsequent synthesis of a corresponding color image . Most of these works use GANs , with the exception of Yan et al . appendix : Appendix C : Effect of Sharpening Images . Some examples of generated images before ( top row ) and after ( bottom row ) sharpening images using an adversarial network trained on residuals of a Laplacian pyramid conditioned on the skipthought vectors of the captions . [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] It is well known that the L2 loss – and L1 , see Figure [ reference ] – produces blurry results on image generation problems . Although these losses fail to encourage high - frequency crispness , in many cases they nonetheless accurately capture the low frequencies . Our work is therefore related to lot of recent work in adversarial learning . For example , techniques have been proposed to improve adversarial learning for image generation as well as for training better image generative model . also highlights that the adversarial learning can improve image classification in a semi - supervised setting . On the other hand , GANs also demonstrate potential in generating images for specific fields . Pathak propose an encoder - decoder method for image inpainting , where GANs are used as the image generator . Similarly , Yeh improve the inpainting performance by introducing two loss types . Empirically , we show that employing a principled Bayesian approach is more robust regardless of the scarcity of the data . Although vaes have been extensively studied for image modeling and generation , there is surprisingly little work applying vaes to recommender systems . We find that two adjustments are essential to getting state - of - the - art results with vaes on this task : The Spatial Colour Distribution Descriptor ( SpCD ) , a content based image retrieval was used to mathematically check the similarity of the bag context shape grammar generated images . The outcome of this study shows that BCSGs are good for the generation of images that are similar . This type of approach can be useful in the generation of distractor ( similar ) images for visual password systems and others . Generating images from natural language description has drawn a lot of attention in the research community for its practical usefulness and for understanding the method in which the model relates text with visual concepts by synthesizing them . Deep generative models have been successfully employed to address this task , which formulates the problem as a translation task from text to image . One of these areas , that of the of generation of realistic three-dimensional images , is the subject matter of this work . More specifically , this work is concerned with a particular method of generation of such images , known as Raytracmg , which has produced some of the most realistic computer generated images to date . Unfortunately , because of a large appetite for numeric calculation , raytracmg has tended to be restricted to mainframe computers . The proposed algorithms enable the automatic generation of visually pleasant mosaics for both compositing as well as stitching . Several applications are demonstrated for the proposed mosaicing algorithms such as image compositing for visual effects , creation of new images without user/with least user intervention for multimedia applications and panorama generation with wide FOV . To avoid overfitting limited ADC encodings , an unlimited amount of random latent vectors and unpaired ADC-T2w Images are utilized in the unsupervised training process for learning the marginal image distributions of real images . To improve the robustness for training the synthesizer , we decompose the difficult task of generating full-size images into several simpler tasks which generate sub-images only . A StitchLayer is then employed to seamlessly fuse sub-images together in an interlaced manner into a full-size image . We address the problem of learning dynamic patterns from unlabeled video sequences , either in the form of generating new video sequences , or recovering incomplete video sequences . This problem is challenging because the appearances and motions in the video sequences can be very complex . Generating visual images automatically given semantic attributes description helps building friendly human-machine interface as criminal suspects depicting and customer-oriented product design . However , this is a hard task since there exist great semantic and structure gaps between attributes description and visual images , which further cause great inference uncertainty in transforming from attribute description to vivid visual images . We also investigate the possibility of using semi-supervised learning for an effective disentangling even using few labels . We leverage the linearization property of the latent spaces for semantic image editing and generation of new images . We validate our approach on CelebA , Yale-B and NORB by measuring the efficiency of information separation via classification metrics , visual image manipulation and data augmentation . Also , the reprojection theory does not assume that the input images are captured from cameras with identical internal calibration parameters . Thus , we apply our technique to two different domains : 1 ) generation of novel images from a stereo pair ; and 2 ) generation of virtual walk-through sequence with a monocular image sequence as input . The third phase is the weather prediction , which applies numerical equations to measured conditions in order to project future weather conditions . The final phase is the generation of visual images of the processed data products that are analyzed to make predictions . Each phase of activity is performed by one or more application components . subsection : LSUN As visual quality of samples from generative image models has improved , concerns of over - fitting and memorization of training samples have risen . To demonstrate how our model scales with more data and higher resolution generation , we train a model on the LSUN bedrooms dataset containing a little over 3 million training examples . Generative image modeling is a central problem in unsupervised learning . Probabilistic density models can be used for a wide variety of tasks that range from image compression and forms of reconstruction such as image inpainting ( e.g. , see Figure [ reference ] ) and deblurring , to generation of new images . When the model is conditioned on external information , possible applications also include creating images based on text descriptions or simulating future frames in a planning task . section : Model Our proposed model defines a generative process of images conditioned on captions . In particular , captions are represented as a sequence of consecutive words and images are represented as a sequence of patches drawn on a canvas over time . We present FusedGAN , a deep network for conditional image synthesis with controllable sampling of diverse images . Fidelity , diversity and controllable sampling are the main quality measures of a good image generation model . A new approach to classifier synthesis was proposed by Polkowski and in this work we propose an implementation of this idea . The idea is based on usage of a dual indiscernibility matrix which allows to determine for each test object in the data , pairs of training objects which cover in a sense the given test object . We construct a variant of GANs employing label conditioning that results in 128x128 resolution image samples exhibiting global coherence . We expand on previous work for image quality assessment to provide two new analyses for assessing the discriminability and diversity of samples from class-conditional image synthesis models . These analyses demonstrate that high resolution samples provide class information not present in low resolution samples . In the process , we extend recent GAN developments to the group-equivariant setting . We demonstrate the utility of our methods by improving both sample fidelity and diversity in the class-conditional synthesis of a diverse set of globally-symmetric imaging modalities . Existing pipelines typically perform cloud removal with simple temporal composites and hand-crafted filters . In contrast , we cast the problem of cloud removal as a conditional image synthesis challenge , and we propose a trainable spatiotemporal generator network ( STGAN ) to remove clouds . We train our model on a new large-scale spatiotemporal dataset that we construct , containing 97640 image pairs covering all continents . Thus a critical task in material design is establishing the processing-structure relationship , which requires domain expertise and techniques that can model the high-dimensional material microstructure . This work proposes a deep learning based approach that models the processing-structure relationship as a conditional image synthesis problem . In particular , we develop an auxiliary classifier Wasserstein GAN with gradient penalty ( ACWGAN-GP ) to synthesize microstructures under a given processing condition . Coupling the high-fidelity generation capabilities of label-conditional image synthesis methods with the flexibility of unconditional generative models , we propose a semantic bottleneck GAN model for unconditional synthesis of complex scenes . We assume pixel-wise segmentation labels are available during training and use them to learn the scene structure . We find that applying orthogonal regularization to the generator renders it amenable to a simple “ truncation trick , ” allowing fine control over the trade - off between sample fidelity and variety by reducing the variance of the Generator ’s input . Our modifications lead to models which set the new state of the art in class - conditional image synthesis . When trained on ImageNet at 128 128 resolution , our models ( BigGANs ) achieve an Inception Score ( IS ) of 166.5 and Fréchet Inception Distance ( FID ) of 7.4 , improving over the previous best IS of 52.52 and FID of 18.65 . This mode seeking regularization term is readily applicable to various conditional generation tasks without imposing training overhead or modifying the original network structures . We validate the proposed algorithm on three conditional image synthesis tasks including categorical generation , image - to - image translation , and text - to - image synthesis with different baseline models . Both qualitative and quantitative results demonstrate the effectiveness of the proposed regularization method for improving diversity without loss of quality . We exhibit relativizations which show that this question can not be easily answered . We also relate construction , generation , and categorical generation ( generation with unique computation path for each string generated , i.e. no repetitions ) , to the existence of sparse languages in NP-P and in DP-P. We give a characterization of the languages in NP which can be efficiently constructed from which it can be deduced that all languages in NP can be efficiently constructed if and only if all languages in P can be efficiently constructed . We also deal with parameter-based generation , with construction in the polynomial time hierarchy , and with the existence of certain types of generators viewed as NP machines . Categorical Image Classification needs thousands of images to train . Also , System needs more time to extract the features as well as classification . Uncertainty analyses of the predictions are performed to evaluate the quality of classifications . Sample points of a categorical attribute are considered as the hard , or primary , information while a categorical map is used for determine the soft , or the secondary , information . The soft information is incorporated in the indicator simulation procedure as prior mean values , taken from a probability distribution function , related to the hard data . This paper introduces a novel method for categorical image labeling , where each pixel is uniquely assigned to one of a set of unordered , discrete labels . Starting from provided label-depending pixel likelihoods we ( a ) exploit a segment hierarchy as spatial support to define powerful priors and ( b ) introduce an efficient and effective inference method , that can be implemented in a few lines of code . As a pointer to a more comprehensive effort , it is nonetheless intended to provide a useful resource documenting the emergence and rapid growth of scientific visualization . The hypertext dissertation is accessible at the following URL : http://www.eas.asu.edu/$\sim$vizier/voegele/tessviz.html The serendipity of using an annotated scientific visualization bibliography as an example categorical data set for developing categorical visualization methods is duly noted . This mode seeking regularization term is readily applicable to various conditional generation tasks without imposing training overhead or modifying the original network structures . We validate the proposed algorithm on three conditional image synthesis tasks including categorical generation , image - to - image translation , and text - to - image synthesis with different baseline models . Both qualitative and quantitative results demonstrate the effectiveness of the proposed regularization method for improving diversity without loss of quality . For image synthesis , cGANs can be applied to various tasks with different conditional contexts . With class labels , cGANs can be applied to categorical image generation . With text sentences , cGANs can be applied to text - to - image synthesis .
As a reaction to their changing environment , many contemporary artists are using the newly available tools of three of today ’s most important technologies — video , computers and communications technology — in their search for new forms of creative expression , video art has evolved from artists ’ use of the tools of television for their own creative ends . Computers offer unlimited possibilities for image generation and the manipulation of vast quantities of information in the creative process . Communication technology allows interactivity in both these fields as well as the creation of new work spaces that eliminate distances between collaborators .
The counterpart to a generative model is its statistical inverse , called a recognition model ( Hinton & Zemel , 1994 ) . This takes low-level activities and produces probability distributions over the entities in the world that could have led to them , expressed as activities of neurons in higher visual areas that model the image generation process . Even if a generative model is computationally tractable , its associated recognition model may not be .
The invention discloses a somatosensory interactive system . The somatosensory interactive system comprises a video collecting device which is used for capturing dance actions of a user , the video collecting device is connected with a video collecting card which is used for transforming dance action analog signals shot by the video collecting device to digital signals , the video collecting card is connected with an action processing module which is used for compressing and encoding the digital signals output by the video collecting card into digital videos and recording the beautiful dance postures after analytically processing dance postures , the action processing module is connected with a main control server , the main control server is connected with an image generating module , the image generating module is connected with a projector , and a projection screen supporting the use of the projector display contrast images to a user . According to the somatosensory interactive system , by recording the beautiful dance postures , dance postures captured in real time are compared with the beautiful dance postures recorded before , the contrast result is presented to the user in a graph form , and the user is helped to correct the dance postures in real time .
For a given image generation problem , the intrinsic image manifold is often low dimensional . We use the intuition that it is much better to train the GAN generator by minimizing the distributional distance between real and generated images in a small dimensional feature space representing such a manifold than on the original pixel-space .
An image generation device ( 100 ) generates an output image on the basis of an input image captured by a camera ( 2 ) attached to a shovel ( 60 ) . The image generation device ( 100 ) is equipped with an output image generation means ( 11 ) that generates an output image by associating the values for input pixels in the input image with the values for output pixels in the output image . The value of an output pixel specified with integer coordinate points in an output image plane is determined on the basis of the values of two input pixels , which are specified by two integer coordinate points ( B , D ) selected from four integer coordinate points ( A-D ) located around a real number coordinate point ( P ) in an input image plane corresponding to those integer coordinate points of the output pixel .
In 2000 , a new system was developed , built upon a Java prototype of a quality control graphical client interface for wind observations and an object-relational database . The objective was to integrate them in a distributed object approach with the legacy code responsible for the actual real-time wind analysis and image product generation . Common Object Request Broker Architecture ( CORBA ) was evaluated , but Java Remote Method Invocation ( AMI ) offered important advantages in terms of reuse and deployment .
A method according to any of claims 1 - 2 , wherein the collecting protocol includes one of a set of system protocols for collecting ( 100 ) the image forming subekta.4 selected for scanning . A method according to any of claims 1 - 2 , further comprising : generating two or more cards dose for two or more protocols collection system ( 100 ) of the image forming generation , at least one of the two or more sound cards or two or more cards relationship contrast / noise ratio for two or more collection protocols ; iodnovremennoe dose map display and , at least one of the noise maps or maps relationships contrast / shum.5 . A method according to any of claims 1 - 2 , further comprising : receiving input indicative of a change at least one parameter value acquisition protocol , and generation and display of the dose maps and the noise maps on the basis izmeneniya.6 .
In this paper , we employ Convolutional Neural Network method to design a classifier , by which gram staining images can be identified as normal group and disease model group effectively and correctly . And image generate method is used to improve classification accuracy . The fecal gram stain smears of health rats and irritable bowel syndrome model rats are employed to validate the proposed model .
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 . Researchers have successfully applied GANs to various applications such as image generation denton2015deep , radford2015unsupervised , zhao2016energy , image editing zhu2016generative and feature learning salimans2016improved , donahue2016adversarial . Recent work isola2016image , sangkloy2016scribbler , karacan2016learning adopt conditional GANs mirza_arxiv14 for these image - to - image translation problems isola2016image , but they require input - output image pairs for training , which is in general not available in domain adaptation problems .
[ reference ] . The SAN consists of three major components : ( 1 ) the image model , which uses a CNN to extract high level image representations , e.g. one vector for each region of the image ; ( 2 ) the question model , which uses a CNN or a LSTM to extract a semantic vector of the question and ( 3 ) the stacked attention model , which locates , via multi - step reasoning , the image regions that are relevant to the question for answer prediction . As illustrated in Fig .
The non - parametric models often do matching from a database of existing images , often matching patches of images , and have been used in texture synthesis efros1999texture , super - resolution freeman2002example and in - painting hays2007scene . Parametric models for generating images has been explored extensively ( for example on MNIST digits or for texture synthesis portilla2000parametric ) . However , generating natural images of the real world have had not much success until recently .
[ reference ] synthesize 128×128 images of birds and people conditioned on text descriptions and on spatial constraints such as bounding boxes or keypoints . Wang and Gupta [ reference ] synthesize 128×128 images of indoor scenes by factorizing the image generation process into synthesis of a normal map and subsequent synthesis of a corresponding color image . Most of these works use GANs , with the exception of Yan et al .
appendix : Appendix C : Effect of Sharpening Images . Some examples of generated images before ( top row ) and after ( bottom row ) sharpening images using an adversarial network trained on residuals of a Laplacian pyramid conditioned on the skipthought vectors of the captions . [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ]
It is well known that the L2 loss – and L1 , see Figure [ reference ] – produces blurry results on image generation problems . Although these losses fail to encourage high - frequency crispness , in many cases they nonetheless accurately capture the low frequencies .
Our work is therefore related to lot of recent work in adversarial learning . For example , techniques have been proposed to improve adversarial learning for image generation as well as for training better image generative model . also highlights that the adversarial learning can improve image classification in a semi - supervised setting .
On the other hand , GANs also demonstrate potential in generating images for specific fields . Pathak propose an encoder - decoder method for image inpainting , where GANs are used as the image generator . Similarly , Yeh improve the inpainting performance by introducing two loss types .
Empirically , we show that employing a principled Bayesian approach is more robust regardless of the scarcity of the data . Although vaes have been extensively studied for image modeling and generation , there is surprisingly little work applying vaes to recommender systems . We find that two adjustments are essential to getting state - of - the - art results with vaes on this task :
The Spatial Colour Distribution Descriptor ( SpCD ) , a content based image retrieval was used to mathematically check the similarity of the bag context shape grammar generated images . The outcome of this study shows that BCSGs are good for the generation of images that are similar . This type of approach can be useful in the generation of distractor ( similar ) images for visual password systems and others .
Generating images from natural language description has drawn a lot of attention in the research community for its practical usefulness and for understanding the method in which the model relates text with visual concepts by synthesizing them . Deep generative models have been successfully employed to address this task , which formulates the problem as a translation task from text to image .
One of these areas , that of the of generation of realistic three-dimensional images , is the subject matter of this work . More specifically , this work is concerned with a particular method of generation of such images , known as Raytracmg , which has produced some of the most realistic computer generated images to date . Unfortunately , because of a large appetite for numeric calculation , raytracmg has tended to be restricted to mainframe computers .
The proposed algorithms enable the automatic generation of visually pleasant mosaics for both compositing as well as stitching . Several applications are demonstrated for the proposed mosaicing algorithms such as image compositing for visual effects , creation of new images without user/with least user intervention for multimedia applications and panorama generation with wide FOV .
To avoid overfitting limited ADC encodings , an unlimited amount of random latent vectors and unpaired ADC-T2w Images are utilized in the unsupervised training process for learning the marginal image distributions of real images . To improve the robustness for training the synthesizer , we decompose the difficult task of generating full-size images into several simpler tasks which generate sub-images only . A StitchLayer is then employed to seamlessly fuse sub-images together in an interlaced manner into a full-size image .
We address the problem of learning dynamic patterns from unlabeled video sequences , either in the form of generating new video sequences , or recovering incomplete video sequences . This problem is challenging because the appearances and motions in the video sequences can be very complex .
Generating visual images automatically given semantic attributes description helps building friendly human-machine interface as criminal suspects depicting and customer-oriented product design . However , this is a hard task since there exist great semantic and structure gaps between attributes description and visual images , which further cause great inference uncertainty in transforming from attribute description to vivid visual images .
We also investigate the possibility of using semi-supervised learning for an effective disentangling even using few labels . We leverage the linearization property of the latent spaces for semantic image editing and generation of new images . We validate our approach on CelebA , Yale-B and NORB by measuring the efficiency of information separation via classification metrics , visual image manipulation and data augmentation .
Also , the reprojection theory does not assume that the input images are captured from cameras with identical internal calibration parameters . Thus , we apply our technique to two different domains : 1 ) generation of novel images from a stereo pair ; and 2 ) generation of virtual walk-through sequence with a monocular image sequence as input .
The third phase is the weather prediction , which applies numerical equations to measured conditions in order to project future weather conditions . The final phase is the generation of visual images of the processed data products that are analyzed to make predictions . Each phase of activity is performed by one or more application components .
subsection : LSUN As visual quality of samples from generative image models has improved , concerns of over - fitting and memorization of training samples have risen . To demonstrate how our model scales with more data and higher resolution generation , we train a model on the LSUN bedrooms dataset containing a little over 3 million training examples .
Generative image modeling is a central problem in unsupervised learning . Probabilistic density models can be used for a wide variety of tasks that range from image compression and forms of reconstruction such as image inpainting ( e.g. , see Figure [ reference ] ) and deblurring , to generation of new images . When the model is conditioned on external information , possible applications also include creating images based on text descriptions or simulating future frames in a planning task .
section : Model Our proposed model defines a generative process of images conditioned on captions . In particular , captions are represented as a sequence of consecutive words and images are represented as a sequence of patches drawn on a canvas over time .
We present FusedGAN , a deep network for conditional image synthesis with controllable sampling of diverse images . Fidelity , diversity and controllable sampling are the main quality measures of a good image generation model .
A new approach to classifier synthesis was proposed by Polkowski and in this work we propose an implementation of this idea . The idea is based on usage of a dual indiscernibility matrix which allows to determine for each test object in the data , pairs of training objects which cover in a sense the given test object .
We construct a variant of GANs employing label conditioning that results in 128x128 resolution image samples exhibiting global coherence . We expand on previous work for image quality assessment to provide two new analyses for assessing the discriminability and diversity of samples from class-conditional image synthesis models . These analyses demonstrate that high resolution samples provide class information not present in low resolution samples .
In the process , we extend recent GAN developments to the group-equivariant setting . We demonstrate the utility of our methods by improving both sample fidelity and diversity in the class-conditional synthesis of a diverse set of globally-symmetric imaging modalities .
Existing pipelines typically perform cloud removal with simple temporal composites and hand-crafted filters . In contrast , we cast the problem of cloud removal as a conditional image synthesis challenge , and we propose a trainable spatiotemporal generator network ( STGAN ) to remove clouds . We train our model on a new large-scale spatiotemporal dataset that we construct , containing 97640 image pairs covering all continents .
Thus a critical task in material design is establishing the processing-structure relationship , which requires domain expertise and techniques that can model the high-dimensional material microstructure . This work proposes a deep learning based approach that models the processing-structure relationship as a conditional image synthesis problem . In particular , we develop an auxiliary classifier Wasserstein GAN with gradient penalty ( ACWGAN-GP ) to synthesize microstructures under a given processing condition .
Coupling the high-fidelity generation capabilities of label-conditional image synthesis methods with the flexibility of unconditional generative models , we propose a semantic bottleneck GAN model for unconditional synthesis of complex scenes . We assume pixel-wise segmentation labels are available during training and use them to learn the scene structure .
We find that applying orthogonal regularization to the generator renders it amenable to a simple “ truncation trick , ” allowing fine control over the trade - off between sample fidelity and variety by reducing the variance of the Generator ’s input . Our modifications lead to models which set the new state of the art in class - conditional image synthesis . When trained on ImageNet at 128 128 resolution , our models ( BigGANs ) achieve an Inception Score ( IS ) of 166.5 and Fréchet Inception Distance ( FID ) of 7.4 , improving over the previous best IS of 52.52 and FID of 18.65 .
This mode seeking regularization term is readily applicable to various conditional generation tasks without imposing training overhead or modifying the original network structures . We validate the proposed algorithm on three conditional image synthesis tasks including categorical generation , image - to - image translation , and text - to - image synthesis with different baseline models . Both qualitative and quantitative results demonstrate the effectiveness of the proposed regularization method for improving diversity without loss of quality .
We exhibit relativizations which show that this question can not be easily answered . We also relate construction , generation , and categorical generation ( generation with unique computation path for each string generated , i.e. no repetitions ) , to the existence of sparse languages in NP-P and in DP-P. We give a characterization of the languages in NP which can be efficiently constructed from which it can be deduced that all languages in NP can be efficiently constructed if and only if all languages in P can be efficiently constructed . We also deal with parameter-based generation , with construction in the polynomial time hierarchy , and with the existence of certain types of generators viewed as NP machines .
Categorical Image Classification needs thousands of images to train . Also , System needs more time to extract the features as well as classification .
Uncertainty analyses of the predictions are performed to evaluate the quality of classifications . Sample points of a categorical attribute are considered as the hard , or primary , information while a categorical map is used for determine the soft , or the secondary , information . The soft information is incorporated in the indicator simulation procedure as prior mean values , taken from a probability distribution function , related to the hard data .
This paper introduces a novel method for categorical image labeling , where each pixel is uniquely assigned to one of a set of unordered , discrete labels . Starting from provided label-depending pixel likelihoods we ( a ) exploit a segment hierarchy as spatial support to define powerful priors and ( b ) introduce an efficient and effective inference method , that can be implemented in a few lines of code .
As a pointer to a more comprehensive effort , it is nonetheless intended to provide a useful resource documenting the emergence and rapid growth of scientific visualization . The hypertext dissertation is accessible at the following URL : http://www.eas.asu.edu/$\sim$vizier/voegele/tessviz.html The serendipity of using an annotated scientific visualization bibliography as an example categorical data set for developing categorical visualization methods is duly noted .
This mode seeking regularization term is readily applicable to various conditional generation tasks without imposing training overhead or modifying the original network structures . We validate the proposed algorithm on three conditional image synthesis tasks including categorical generation , image - to - image translation , and text - to - image synthesis with different baseline models . Both qualitative and quantitative results demonstrate the effectiveness of the proposed regularization method for improving diversity without loss of quality .
For image synthesis , cGANs can be applied to various tasks with different conditional contexts . With class labels , cGANs can be applied to categorical image generation . With text sentences , cGANs can be applied to text - to - image synthesis .
The Many Worlds of Art, Science and the New Technologies Recognition in hierarchical models Somatosensory interactive system Image Generation Via Minimizing Fréchet Distance in Discriminator Feature Space Image generating device and operation assistance system A real-time distributed analysis automation for hurricane surface wind observations evaluation protocol collection apparatus Gram staining of intestinal flora classification based on convolutional neural network CyCADA: Cycle-Consistent Adversarial Domain Adaptation Stacked Attention Networks for Image Question Answering Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Photographic Image Synthesis with Cascaded Refinement Networks Generating Images from Captions with Attention Image-to-Image Translation with Conditional Adversarial Networks A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro Variational Autoencoders for Collaborative Filtering The Similarity of Images Generated by Bag Context Shape Grammars Interpretable Text-to-Image Synthesis with Hierarchical Semantic Layout Generation Ray tracing under the microcomputer Development of Efficient Algorithms for Seamless Image Mosaicing Semi-supervised mp-MRI Data Synthesis with StitchLayer and Auxiliary Distance Maximization Learning Generator Networks for Dynamic Patterns Attributes Consistent Faces Generation Under Arbitrary Poses DualDis: Dual-Branch Disentangling with Adversarial Learning A new camera projection model and its application in reprojection Dynamic, Adaptive Workflows for Mesoscale Meteorology Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Pixel Recurrent Neural Networks Generating Images from Captions with Attention Semi-supervised FusedGAN for Conditional Image Generation A New Classifier Based on the Dual Indiscernibility Matrix Conditional Image Synthesis with Auxiliary Classifier GANs Group Equivariant Generative Adversarial Networks Cloud Removal in Satellite Images Using Spatiotemporal Generative Networks A Conditional Generative Model for Predicting Material Microstructures from Processing Methods Semantic Bottleneck Scene Generation Large Scale GAN Training for High Fidelity Natural Image Synthesis Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis Efficient Language Instance Generation Categorical Image Classification Based On Representational Deep Network (RESNET) SPATIAL MODELING OF CATEGORICAL ATTRIBUTES USING INDICATOR SIMULATION AND SOFT INFORMATION WITH UNCERTAINTY ANALYSES Hierarchical Segment Support for Categorical Image Labeling Tessellation of categorical data: an example using bibliographic data Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis
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["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science","Engineering"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics","Medicine"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Environmental Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science","Engineering"] ["Computer Science"] ["Computer Science","Engineering","Physics","Mathematics"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"]
1991 1997 2011 2020 2011 2002 2010 2017 2018 2016 2016 2017 2016 2017 2017 2017 2018 2020 2019 1989 2017 2018 2019 2018 2019 1998 2007 2016 2016 2016 2018 2016 2017 2020 2019 2019 2019 2019 2019 1988 2019 2016 2013 1996 2019
Healthcom ICML 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICLR 2017 IEEE International Conference on Computer Vision (ICCV) ICLR 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE International Conference on Computer Vision (ICCV) WWW Explainable AI WACV ACCV (2) ICPR Workflows for e-Science, Scientific Workflows for Grids ICLR ICML ICLR ICIST ICML ICLR 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ICCV Workshops 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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