Abstract Neural learning methods have been shown to be effective in style transfer . These methods , which are called NST , aim to synthesize a new image that retains the high-level structure of a content image while keeps the low-level features of a style image . Recent research endeavors have shown the potential of using feed-forward convolutional neural networks to accomplish fast style transfer for images . In this work , we take one step further to explore the possibility of exploiting a feed-forward network to perform style transfer for videos and simultaneously maintain temporal consistency among stylized video frames . Our feed-forward network is trained by enforcing the outputs of consecutive frames to be both well stylized and temporally consistent . Consequently , supervised source separation depends on the availability of paired mixture-clean training examples . In this paper , we interpret source separation as a style transfer problem . We present a variational auto-encoder network that exploits the commonality across the domain of mixtures and the domain of clean sounds and learns a shared latent representation across the two domains . First , they render a novel saliency detection algorithm to automatically perceive the visual attention of an image . Then , different from conventional style transfer algorithm in which style transferring is applied uniformly across all image regions , the authors apply a saliency algorithm to guide the style transferring process , enabling different types of style transferring to occur in different regions . Extensive experiments show that the proposed saliency detection algorithm and the style transfer algorithm are superior in performance and efficiency . We design an analytic pipeline which first generates synthetic retinal images from clinical images ; a subsequent verification step is applied . In the synthesizing step we merge GANs ( DCGANs and WGANs architectures ) and style transferring for the image generation , whereas the verified step controls the accuracy of the generated images . We find that the generated images contain sufficient pathological details to facilitate ophthalmologists ’ task of disease classification and in discovery of disease relevant features . Generalized from these methods , in this work , we derive the form of transformation matrix theoretically and present an arbitrary style transfer approach that learns the transformation matrix with a feed-forward network . Our algorithm is highly efficient yet allows a flexible combination of multi-level styles while preserving content affinity during style transfer process . We demonstrate the effectiveness of our approach on four tasks : artistic style transfer , video and photo-realistic style transfer as well as domain adaptation , including comparisons with the state-of-the-art methods . To minimize such a risk and reduce the influence of spatial differences between the input image and the style image , we introduce a spatial transformation strategy in this paper before style transfer . By maximizing the normalized cross-correlation of the feature maps , we propose to apply a series of affine transformations to the reference image and use those spatially-transformed images as the style reference to achieve a robust style transfer , when the semantically segmented regions of both the input image and the reference image are used as inputs to the pre-trained convolutional neural network . Consequently , the incurred ghost shadows can be minimized or eliminated . Given that current tasks do not represent real use cases of style transfer , current auto-evaluation approach is flawed . This discussion aims to bring researchers to think about the future of style transfer and style transfer evaluation research . In this work , we analyze the architecture of the style transfer network and divide it into three parts : feature extraction , style transfer , and image reconstruction . And a novel way is proposed to accelerate the style transfer operation and reduce the memory usage at run-time by conducting the super-resolution style transfer network ( SRSTN ) , which can generate super-resolution stylized images . Compared with other style transfer networks , SRSTN can produce competitive quality resulting images with a faster speed as well as less memory usage . For semantic segmentation , we leverage DABNet that achieves high accuracy , yet only has 0.76 million parameters and runs at 104 FPS . For the style transfer step , we use the popular real-time method proposed by Johnson et al. [ 7 ] . We evaluated CBS on a video of the CityScapes dataset and observed high-quality localized style transfer results for different object classes and real-time performance . 2 , 500 development and 5 , 000 test sentences have been translated from English into 14 languages by professional translators , making results across different languages directly comparable . Note that no human translated training data is provided ; instead , different systems are to use English training data from MultiNLI , and their transfer performance is evaluated on the rest of languages . We train a classifier on top of our multilingual encoder using the usual combination of the two sentence embeddings : , where and are the premise and hypothesis . paragraph : Controllable text generation . Many studies have been carried out in the framework of style transfer , which is the task of rephrasing the text so that it contains specific styles such as sentiment . Recent work uses artificial tokens SennrichHB16 , JohnsonSLKWCTVW17 , variational auto - encoders HuYLSX17 , adversarial training FuTPZY18 , TsvetkovBSP18 , or prior knowledge LiJHL18 to separate the content and style on the encoder side . 51 series of singlechip and E2PROM to carry out the realization method of serial data transmission are disvussed , and its hardware interface circuit and interface program are provided . A 1-bit serial transceiver chip set for the full-color moving picture was developed . It introduced a new data coding for the serial transmission and novel circuits to realize high frequency operation with low power consumption . It was able to transmit the XGA picture with 480mW. A high-speed bus system combines at least one parallel bus and at least one serial bus for simultaneously transmitting data in series and in parallel . The serial data transfer follows a trunking scheme and data and/or programmes are transmitted normally at the same time in parallel by the parallel bus . The logic of the parallel bus is used for the trunking scheme for transmitting data in parallel as well as in series . This paper describes flow and timeout control methods in serial data transfer through WIN32c , and provides the implementation steps in practical application . The output of the system is to some real-world quantity external to the electronic system . The second way is serial transfer of data . A clock rate determines the rate at which the bits are transferred . Specifically , we examine a structure which employs serial transfer from the plaintext queue to the ciphertext queue [ 1 ] . Serial transfer generally requires a simpler circuit than parallel transfer [ 2 ] , but it has a clock limitation which constrains the system efficiency . In this paper , the hardware structure and method of hardware implementation for serial transfer SCFB mode is investigated . Ternary logic has been evolved from binary logic due to its many advantages as it is energy efficient , less complex and faster speed for serial transfer . Due to these technological advancements ternary circuits have attracted many researchers to implement ternary circuits . The crossbar is a vital component of in any NoC router . In this work , we have designed a crossbar interconnect for serial bit data transfer and 128-parallel bit data transfer . We have shown comparision between power and delay for the serial bit and parallel bit data transfer through crossbar switch . This paper considers equalization of the slow fading channel for a serial data transmission application . Linear and decision-feedback adaptive equalization techniques are contrasted . Standards and guidelines that include information dealing with safety-related communications and the design of safety-related communication systems are introduced . The typical message error types or threats relating to serial mode transmission as well as defence methods against these threats are also introduced . There are several safety buses available for safety-related machine and automation applications . The AMBA 2.0 bus standards is adopted to achieve the communication between the peripheral devices and the internal system through SPI . As fore data transmission section , the traditional design method was abandoned , which needs a specific shift regis ter for serial/parallel transfer . The shifting transmission register and receive register are put together by the reuse registers to i m prove the speed and save the hardware resources . Utilizing deep learning techniques to generate musical contents has caught wide attention in recent years . Within this context , this paper investigates a specific problem related to music generation , music style transfer . This practical problem aims to alter the style of a given music piece from one to another while preserving the essence of that piece , such as melody and chord progression . GAN-based models employing several generators and some form of cycle consistency loss have been among the most successful for image domain transfer . In this paper we apply such a model to symbolic music and show the feasibility of our approach for music genre transfer . Evaluations using separate genre classifiers show that the style transfer works well . Over the years , they have been extensively studied . Recently , deep neural networks have attracted much research attention for both face alignment and face transfer . With the prevalence of the consumer depth sensor , depth-based face alignment and cross-modal ( image and depth ) are increasingly important . A multilinear model can be estimated from a Cartesian product of examples ( identities × expressions × visemes ) with techniques from statistical analysis , but only after careful preprocessing of the geometric data set to secure one-to-one correspondence , to minimize cross-coupling artifacts , and to fill in any missing examples . Face Transfer offers new solutions to these problems and links the estimated model with a face-tracking algorithm to extract pose , expression , and viseme parameters . Compared with previous 3D facial databases , for every person in our database , there is a much richer matching collection of expressions , enabling depiction of most human facial actions . We demonstrate the potential of FaceWarehouse for visual computing with four applications : facial image manipulation , face component transfer , real-time performance-based facial image animation , and facial animation retargeting from video to image . To leverage the complementary discriminating capability of different feature layers , a hierarchical structure is proposed for the discriminator . We have experimentally demonstrated that HAN is an effective framework for typeface transfer and characters restoration .
Abstract Neural learning methods have been shown to be effective in style transfer . These methods , which are called NST , aim to synthesize a new image that retains the high-level structure of a content image while keeps the low-level features of a style image .
Recent research endeavors have shown the potential of using feed-forward convolutional neural networks to accomplish fast style transfer for images . In this work , we take one step further to explore the possibility of exploiting a feed-forward network to perform style transfer for videos and simultaneously maintain temporal consistency among stylized video frames . Our feed-forward network is trained by enforcing the outputs of consecutive frames to be both well stylized and temporally consistent .
Consequently , supervised source separation depends on the availability of paired mixture-clean training examples . In this paper , we interpret source separation as a style transfer problem . We present a variational auto-encoder network that exploits the commonality across the domain of mixtures and the domain of clean sounds and learns a shared latent representation across the two domains .
First , they render a novel saliency detection algorithm to automatically perceive the visual attention of an image . Then , different from conventional style transfer algorithm in which style transferring is applied uniformly across all image regions , the authors apply a saliency algorithm to guide the style transferring process , enabling different types of style transferring to occur in different regions . Extensive experiments show that the proposed saliency detection algorithm and the style transfer algorithm are superior in performance and efficiency .
We design an analytic pipeline which first generates synthetic retinal images from clinical images ; a subsequent verification step is applied . In the synthesizing step we merge GANs ( DCGANs and WGANs architectures ) and style transferring for the image generation , whereas the verified step controls the accuracy of the generated images . We find that the generated images contain sufficient pathological details to facilitate ophthalmologists ’ task of disease classification and in discovery of disease relevant features .
Generalized from these methods , in this work , we derive the form of transformation matrix theoretically and present an arbitrary style transfer approach that learns the transformation matrix with a feed-forward network . Our algorithm is highly efficient yet allows a flexible combination of multi-level styles while preserving content affinity during style transfer process . We demonstrate the effectiveness of our approach on four tasks : artistic style transfer , video and photo-realistic style transfer as well as domain adaptation , including comparisons with the state-of-the-art methods .
To minimize such a risk and reduce the influence of spatial differences between the input image and the style image , we introduce a spatial transformation strategy in this paper before style transfer . By maximizing the normalized cross-correlation of the feature maps , we propose to apply a series of affine transformations to the reference image and use those spatially-transformed images as the style reference to achieve a robust style transfer , when the semantically segmented regions of both the input image and the reference image are used as inputs to the pre-trained convolutional neural network . Consequently , the incurred ghost shadows can be minimized or eliminated .
Given that current tasks do not represent real use cases of style transfer , current auto-evaluation approach is flawed . This discussion aims to bring researchers to think about the future of style transfer and style transfer evaluation research .
In this work , we analyze the architecture of the style transfer network and divide it into three parts : feature extraction , style transfer , and image reconstruction . And a novel way is proposed to accelerate the style transfer operation and reduce the memory usage at run-time by conducting the super-resolution style transfer network ( SRSTN ) , which can generate super-resolution stylized images . Compared with other style transfer networks , SRSTN can produce competitive quality resulting images with a faster speed as well as less memory usage .
For semantic segmentation , we leverage DABNet that achieves high accuracy , yet only has 0.76 million parameters and runs at 104 FPS . For the style transfer step , we use the popular real-time method proposed by Johnson et al. [ 7 ] . We evaluated CBS on a video of the CityScapes dataset and observed high-quality localized style transfer results for different object classes and real-time performance .
2 , 500 development and 5 , 000 test sentences have been translated from English into 14 languages by professional translators , making results across different languages directly comparable . Note that no human translated training data is provided ; instead , different systems are to use English training data from MultiNLI , and their transfer performance is evaluated on the rest of languages . We train a classifier on top of our multilingual encoder using the usual combination of the two sentence embeddings : , where and are the premise and hypothesis .
paragraph : Controllable text generation . Many studies have been carried out in the framework of style transfer , which is the task of rephrasing the text so that it contains specific styles such as sentiment . Recent work uses artificial tokens SennrichHB16 , JohnsonSLKWCTVW17 , variational auto - encoders HuYLSX17 , adversarial training FuTPZY18 , TsvetkovBSP18 , or prior knowledge LiJHL18 to separate the content and style on the encoder side .
51 series of singlechip and E2PROM to carry out the realization method of serial data transmission are disvussed , and its hardware interface circuit and interface program are provided .
A 1-bit serial transceiver chip set for the full-color moving picture was developed . It introduced a new data coding for the serial transmission and novel circuits to realize high frequency operation with low power consumption . It was able to transmit the XGA picture with 480mW.
A high-speed bus system combines at least one parallel bus and at least one serial bus for simultaneously transmitting data in series and in parallel . The serial data transfer follows a trunking scheme and data and/or programmes are transmitted normally at the same time in parallel by the parallel bus . The logic of the parallel bus is used for the trunking scheme for transmitting data in parallel as well as in series .
This paper describes flow and timeout control methods in serial data transfer through WIN32c , and provides the implementation steps in practical application .
The output of the system is to some real-world quantity external to the electronic system . The second way is serial transfer of data . A clock rate determines the rate at which the bits are transferred .
Specifically , we examine a structure which employs serial transfer from the plaintext queue to the ciphertext queue [ 1 ] . Serial transfer generally requires a simpler circuit than parallel transfer [ 2 ] , but it has a clock limitation which constrains the system efficiency . In this paper , the hardware structure and method of hardware implementation for serial transfer SCFB mode is investigated .
Ternary logic has been evolved from binary logic due to its many advantages as it is energy efficient , less complex and faster speed for serial transfer . Due to these technological advancements ternary circuits have attracted many researchers to implement ternary circuits .
The crossbar is a vital component of in any NoC router . In this work , we have designed a crossbar interconnect for serial bit data transfer and 128-parallel bit data transfer . We have shown comparision between power and delay for the serial bit and parallel bit data transfer through crossbar switch .
This paper considers equalization of the slow fading channel for a serial data transmission application . Linear and decision-feedback adaptive equalization techniques are contrasted .
Standards and guidelines that include information dealing with safety-related communications and the design of safety-related communication systems are introduced . The typical message error types or threats relating to serial mode transmission as well as defence methods against these threats are also introduced . There are several safety buses available for safety-related machine and automation applications .
The AMBA 2.0 bus standards is adopted to achieve the communication between the peripheral devices and the internal system through SPI . As fore data transmission section , the traditional design method was abandoned , which needs a specific shift regis ter for serial/parallel transfer . The shifting transmission register and receive register are put together by the reuse registers to i m prove the speed and save the hardware resources .
Utilizing deep learning techniques to generate musical contents has caught wide attention in recent years . Within this context , this paper investigates a specific problem related to music generation , music style transfer . This practical problem aims to alter the style of a given music piece from one to another while preserving the essence of that piece , such as melody and chord progression .
GAN-based models employing several generators and some form of cycle consistency loss have been among the most successful for image domain transfer . In this paper we apply such a model to symbolic music and show the feasibility of our approach for music genre transfer . Evaluations using separate genre classifiers show that the style transfer works well .
Over the years , they have been extensively studied . Recently , deep neural networks have attracted much research attention for both face alignment and face transfer . With the prevalence of the consumer depth sensor , depth-based face alignment and cross-modal ( image and depth ) are increasingly important .
A multilinear model can be estimated from a Cartesian product of examples ( identities × expressions × visemes ) with techniques from statistical analysis , but only after careful preprocessing of the geometric data set to secure one-to-one correspondence , to minimize cross-coupling artifacts , and to fill in any missing examples . Face Transfer offers new solutions to these problems and links the estimated model with a face-tracking algorithm to extract pose , expression , and viseme parameters .
Compared with previous 3D facial databases , for every person in our database , there is a much richer matching collection of expressions , enabling depiction of most human facial actions . We demonstrate the potential of FaceWarehouse for visual computing with four applications : facial image manipulation , face component transfer , real-time performance-based facial image animation , and facial animation retargeting from video to image .
To leverage the complementary discriminating capability of different feature layers , a hierarchical structure is proposed for the discriminator . We have experimentally demonstrated that HAN is an effective framework for typeface transfer and characters restoration .
Direction-aware neural style transfer with texture enhancement Real-Time Neural Style Transfer for Videos A Style Transfer Approach to Source Separation Neural saliency algorithm guide bi-directional visual perception style transfer Synthesizing New Retinal Symptom Images by Multiple Generative Models Learning Linear Transformations for Fast Arbitrary Style Transfer A robust deep style transfer for headshot portraits The Daunting Task of Real-World Textual Style Transfer Auto-Evaluation Accelerate neural style transfer with super-resolution Class-Based Styling: Real-Time Localized Style Transfer with Semantic Segmentation Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond Multi-style Generative Reading Comprehension The Realization Method of Serial Data Transmission about I~2C Bus A 1-bit serial transceiver chip set for full-color XGA pictures of the same high-speed bus system and method for operating Flow and Timeout Control in Serial Data Transfer Through WIN32c Signal Paths from Digital to Analog Implementation of Statistical Cipher Feedback Mode Using Serial Transfer An Approach to Ternary Logic gates using FinFET Modelling and Simulation of 128-Bit Crossbar Switch for Network On Chip Adaptive Equalization of the Slow Fading Channel Safety of Digital Communications in Machines Design and verification of SPI applied to SoC Transferring the Style of Homophonic Music Using Recurrent Neural Networks and Autoregressive Model Symbolic Music Genre Transfer with CycleGAN Joint learning for face alignment and face transfer with depth image Face transfer with multilinear models FaceWarehouse: A 3D Facial Expression Database for Visual Computing Chinese Typeface Transformation with Hierarchical Adversarial Network
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