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检索条件"主题词=variational autoencoder"

共 1539 条记录，以下是921-930 订阅

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Automatic Generation of Semantic Parts for Face Image Synthesis 1

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22nd International Conference on Image Analysis and Processing (ICIAP)

作者： Fontanini, Tomaso Ferrari, Claudio Bertozzi, Massimo Prati, Andrea Univ Parma Dept Engn & Architecture IMP Lab Parma Italy

ISBN: (数字)9783031431487

ISBN: (纸本)9783031431470;9783031431487

Semantic image synthesis (SIS) refers to the problem of generating realistic imagery given a semantic segmentation mask that defines the spatial layout of object classes. Most of the approaches in the literature, other than the quality of the generated images, put effort in finding solutions to increase the generation diversity in terms of style i.e. texture. However, they all neglect a different feature, which is the possibility of manipulating the layout provided by the mask. Currently, the only way to do so is manually by means of graphical users interfaces. In this paper, we describe a network architecture to address the problem of automatically manipulating or generating the shape of object classes in semantic segmentation masks, with specific focus on human faces. Our proposed model allows embedding the mask class-wise into a latent space where each class embedding can be independently edited. Then, a bi-directional LSTM block and a convolutional decoder output a new, locally manipulated mask. We report quantitative and qualitative results on the CelebMask-HQ dataset, which show our model can both faithfully reconstruct and modify a segmentation mask at the class level. Also, we show our model can be put before a SIS generator, opening the way to a fully automatic generation control of both shape and texture. Code available at https://***/TFonta/Semantic-VAE.

关键词： Image Synthesis variational autoencoder Face Editing

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TimToShape: Supporting Practice of Musical Instruments by Visualizing Timbre with 2D Shapes based on Crossmodal Correspondences 23

TimToShape: Supporting Practice of Musical Instruments by Vi...

引用

28th Annual Conference on Intelligent User Interfaces (IUI)

作者： Arai, Kota Hirao, Yutaro Narumi, Takuji Nakamura, Tomohiko Takamichi, Shinnosuke Yoshida, Shigeo Univ Tokyo Tokyo Japan OMRON SINIC X Corp Tokyo Japan

ISBN: (纸本)9798400701061

Timbre is high-dimensional and sensuous, making it difficult for musical-instrument learners to improve their timbre. Although some systems exist to improve timbre, they require expert labeling for timbre evaluation;however, solely visualizing the results of unsupervised learning lacks the intuitiveness of feedback because human perception is not considered. Therefore, we employ crossmodal correspondences for intuitive visualization of the timbre. We designed TimToShape, a system that visualizes timbre with 2D shapes based on the user's input of timbre-shape correspondences. TimToShape generates a shape morphed by linear interpolation according to the timbre's position in the latent space, which is obtained by unsupervised learning with a variational autoencoder (VAE). We confirmed that people perceived shapes generated by TimToShape to correspond more to timbre than randomly generated shapes. Furthermore, a user study of six violin players revealed that TimToShape was well-received in terms of visual clarity and interpretability.

关键词： crossmodal correspondences timbre-shape correspondences timbre musical instrumental practice variational autoencoder

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Fabric Defect Detection by Applying Structural Similarity Index to the Combination of variational Autoencode and Generative Adversarial Network 1st

Fabric Defect Detection by Applying Structural Similarity In...

引用

1st International Conference on Security and Information Technologies with AI, Internet Computing, and Big data Applications (SITAIBA)

作者： Lee, Chin-Feng Chang, Ting-Chia Chaoyang Univ Technol Dept Informat Management Taichung 41349 Taiwan

ISBN: (纸本)9783031054914;9783031054907

Textiles are one of the common necessities in our lives. The quality of textile products is usually closely related to the quality of the fabric materials. Therefore, before the fabric materials are processed, the fabric and textile industry first conducts quality inspections. In the past, traditional methods detect the defects on the fabric surface by human eyes, suffering from the overall inspection standards to be unreliable due to the fatigue and subjective judgment of the inspectors and consuming considerable labor costs and time. Automated detection methods are gradually introduced into the textile industry as one of the important processes. With the rapid advancement of deep learning technology, deep neural networks have caused revolutionary changes in the field of computer vision Therefore, this research employs an unsupervised deep learning model to detect fabric defects by combining the variational autoencoder (VAE) and the generative adversarial network (GAN). The proposed fabric inspection networks The proposed fabric inspection networks also called FINs only use non-defective fabric data to train the model, which solves the problem that traditional detection methods need to collect a large amount of defect data. In the process of model training, we introduced structural similarity index to help the overall model learn the defect-free texture characteristics of fabric surfaces. Finally, through this method, the surface defects can be found and the defective areas can be repaired. After segmentation, the position of the defect can be marked, and the detection result has also reached a certain degree of accuracy.

关键词： Automated fabric detection Deep learning variational autoencoder Generative adversarial network Structural similarity index

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VISinger 2: High-Fidelity End-to-End Singing Voice Synthesis Enhanced by Digital Signal Processing Synthesizer 24

VISinger 2: High-Fidelity End-to-End Singing Voice Synthesis...

引用

Interspeech Conference

作者： Zhang, Yongmao Xue, Heyang Li, Hanzhao Xie, Lei Guo, Tingwei Zhang, Ruixiong Gong, Caixia Northwestern Polytech Univ Audio Speech & Language Proc Grp ASLP NPU Sch Comp Sci Xian Peoples R China DiDi Chuxing Beijing Peoples R China

End-to-end singing voice synthesis (SVS) model VISinger [1] can achieve better performance than the typical two-stage model with fewer parameters. However, VISinger has several problems: text-to-phase problem, the end-to-end model learns the meaningless mapping of text-to-phase;glitches problem, the harmonic components corresponding to the periodic signal of the voiced segment occurs a sudden change with audible artefacts;low sampling rate, the sampling rate of 24KHz does not meet the application needs of high-fidelity generation with the full-band rate (44.1KHz or higher). In this paper, we propose VISinger 2 to address these issues by integrating the digital signal processing (DSP) methods with VISinger. Specifically, inspired by recent advances in differentiable digital signal processing (DDSP) [2], we incorporate a DSP synthesizer into the decoder to solve the above issues. The DSP synthesizer consists of a harmonic synthesizer and a noise synthesizer to generate periodic and aperiodic signals, respectively, from the latent representation z in VISinger. It supervises the posterior encoder to extract the latent representation without phase information and avoid the prior encoder modelling text-to-phase mapping. To avoid glitch artefacts, the HiFiGAN is modified to accept the waveforms generated by the DSP synthesizer as a condition to produce the singing voice. Moreover, with the improved waveform decoder, VISinger 2 manages to generate 44.1kHz singing audio with richer expression and better quality. Experiments on OpenCpop corpus [3] show that VISinger 2 outperforms VISinger, CpopSing and RefineSinger in both subjective and objective metrics. Our audio samples and source code are available (1).

关键词： Singing voice synthesis variational autoencoder adversarial learning

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From Point-wise to Group-wise: A Fast and Accurate Microservice Trace Anomaly Detection Approach 2023

From Point-wise to Group-wise: A Fast and Accurate Microserv...

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31st ACM Joint Meeting of the European Software Engineering Conference / Symposium on the Foundations-of-Software-Engineering (ESEC/FSE)

作者： Xie, Zhe Pei, Changhua Li, Wanxue Jiang, Huai Su, Liangfei Li, Jianhui Xie, Gaogang Pei, Dan Tsinghua Univ Beijing Peoples R China Chinese Acad Sci CNIC Beijing Peoples R China eBay Inc Beijing Peoples R China

ISBN: (纸本)9798400703270

As Internet applications continue to scale up, microservice architecture has become increasingly popular due to its flexibility and logical structure. Anomaly detection in traces that record inter-microservice invocations is essential for diagnosing system failures. Deep learning-based approaches allow for accurate modeling of structural features (i.e., call paths) and latency features (i.e., call response time), which can determine the anomaly of a particular trace sample. However, the point-wise manner employed by these methods results in substantial system detection overhead and impracticality, given the massive volume of traces (billion-level). Furthermore, the point-wise approach lacks high-level information, as identical sub-structures across multiple traces may be encoded differently. In this paper, we introduce the first Group-wise Trace anomaly detection algorithm, named GTrace. This method categorizes the traces into distinct groups based on their shared substructure, such as the entire tree or sub-tree structure. A groupwise variational autoencoder (VAE) is then employed to obtain structural representations. Moreover, the innovative "predicting latency with structure" learning paradigm facilitates the association between the grouped structure and the latency distribution within each group. With the group-wise design, representation caching, and batched inference strategies can be implemented, which significantly reduces the burden of detection on the system. Our comprehensive evaluation reveals that GTrace outperforms state-of-theart methods in both performances (2.64% to 195.45% improvement in AUC metrics and 2.31% to 40.92% improvement in best F-Score) and efficiency (21.9x to 28.2x speedup). We have deployed and assessed the proposed algorithm on eBay's microservices cluster, and our code is available at https://***/NetManAIOps/***.

关键词： microservice trace anomaly detection variational autoencoder

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Representative Data Selection for Efficient Medical Incremental Learning

Representative Data Selection for Efficient Medical Incremen...

引用

45th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Wei, Bo-Quan Chen, Jen-Jee Tseng, Yu-Chee Kuo, Po-Tsun Paul Natl Yang Ming Chiao Tung Univ NYCU Coll Artificial Intelligence Hsinchu Taiwan Acad Sinica Taipei Taiwan Kaohsiung Med Univ Kaohsiung Taiwan Advantech Co AI Res Ctr Taipei Taiwan NYCU Coll Artificial Intelligence Hsinchu Taiwan

ISBN: (纸本)9798350324471

To train a deep neural network relies on a large amount of annotated data. In special scenarios like industry defect detection and medical imaging, it is hard to collect sufficient labeled data all at once. Newly annotated data may arrive incrementally. In practice, we also prefer our target model to improve its capability gradually as new data comes in by quick re-training. This work tackles this problem from a data selection prospective by constraining ourselves to always retrain the target model with a fix amount of data after new data comes in. A variational autoencoder (VAE) and an adversarial network are combined for data selection, achieving fast model retraining. This enables the target model to continually learn from a small training set while not losing the information learned from previous iterations, thus incrementally adapting itself to new-coming data. We validate our framework on the LGG Segmentation dataset for the semantic segmentation task.

关键词： Adversarial network continual learning data management incremental learning variational autoencoder

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A Deep Generative Recommendation Method for Unbiased Learning from Implicit Feedback 23

A Deep Generative Recommendation Method for Unbiased Learnin...

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13th ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR)

作者： Gupta, Shashank Oosterhuis, Harrie de Rijke, Maarten Univ Amsterdam Amsterdam Netherlands Radboud Univ Nijmegen Nijmegen Netherlands

ISBN: (纸本)9798400700736

variational autoencoders (VAEs) are the state-of-the-art model for recommendation with implicit feedback signals. Unfortunately, implicit feedback suffers from selection bias, e.g., popularity bias, position bias, etc., and as a result, training from such signals produces biased recommendation models. Existing methods for debiasing the learning process have not been applied in a generative setting. We address this gap by introducing an inverse propensity scoring (IPS) based method for training VAEs from implicit feedback data in an unbiased way. Our IPS-based estimator for the VAE training objective, VAE-IPS, is provably unbiased w.r.t. selection bias. Our experimental results show that the proposed VAE-IPS model reaches significantly higher performance than existing baselines. Our contributions enable practitioners to combine state-of-the-art VAE recommendation techniques with the advantages of bias mitigation for implicit feedback.

关键词： variational autoencoder Implicit feedback Selection bias

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Cross-utterance Conditioned Coherent Speech Editing 24

Cross-utterance Conditioned Coherent Speech Editing

引用

Interspeech Conference

作者： Yu, Cheng Li, Yang Zu, Weiqin Sun, Fanglei Tian, Zheng Wang, Jun ShanghaiTech Univ Shanghai Peoples R China Univ Manchester Manchester Lancs England UCL London England

Text-based speech editing systems are developed to enable users to modify speech based on the transcript. Existing state-of-the-art editing systems based on neural networks do partial inferences with no exception, that is, only generate new words that need to be replaced or inserted. This manner usually leads to the prosody of the edited part being inconsistent with the surrounding speech and a failure to handle the alteration of intonation. To address these problems, we propose a cross-utterance conditioned coherent speech editing system, that first does the entire reasoning at the inference time. Our proposed system can generate speech by utilizing speaker information, context, acoustic features, and the mel-spectrogram from the original audio. Experiments conducted on subjective and objective metrics demonstrate that our approach outperforms the baseline on various editing operations regarding naturalness and prosody consistency.

关键词： speech editing variational autoencoder

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Personalization Disentanglement for Federated Learning

Personalization Disentanglement for Federated Learning

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yan, Peng Long, Guodong Univ Technol Sydney Fac Engn & IT Sydney NSW Australia

ISBN: (纸本)9781665468916

Personalized federated learning (PFL) jointly trains a variety of local models through balancing between knowledge sharing across clients and model personalization per client. This paper addresses PFL via explicit disentangling latent representations into two parts to capture the shared knowledge and client-specific personalization, which leads to more reliable and effective PFL. The disentanglement is achieved by a novel Federated Dual variational autoencoder (FedDVA), which employs two encoders to infer the two types of representations. FedDVA can produce a better understanding of the trade-off between global knowledge sharing and local personalization in PFL. Moreover, it can be integrated with existing FL methods and turn them into personalized models for heterogeneous downstream tasks. Extensive experiments validate the advantages caused by disentanglement and show that models trained with disentangled representations substantially outperform those vanilla methods.

关键词： Federated Learning Disentanglement variational autoencoder

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Disentangled Representation with Causal Constraints for Counterfactual Fairness 27th

Disentangled Representation with Causal Constraints for Coun...

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27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Xu, Ziqi Liu, Jixue Cheng, Debo Li, Jiuyong Liu, Lin Wang, Ke Univ South Australia Adelaide SA Australia Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9783031333736;9783031333743

Much research has been devoted to the problem of learning fair representations;however, they do not explicitly state the relationship between latent representations. In many real-world applications, there may be causal relationships between latent representations. Furthermore, most fair representation learning methods focus on group-level fairness and are based on correlation, ignoring the causal relationships underlying the data. In this work, we theoretically demonstrate that using the structured representations enables downstream predictive models to achieve counterfactual fairness, and then we propose the Counterfactual Fairness variational autoencoder (CF-VAE) to obtain structured representations with respect to domain knowledge. The experimental results show that the proposed method achieves better fairness and accuracy performance than the benchmark fairness methods.

关键词： Counterfactual Fairness Representation Learning variational autoencoder

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