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检索条件"主题词=Variational autoencoder"

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Four-Legged Gait Control via the Fusion of Computer Vision and Reinforcement Learning 27

Four-Legged Gait Control via the Fusion of Computer Vision a...

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27th International Conference on Information Fusion (FUSION)

作者： Dassori, Ignacio Adams, Martin Vasquez, Jorge Univ Chile Elect Engn Dept Santiago Chile Carnegie Mellon Univ Mech Engn Dept Pittsburgh PA 15213 USA

ISBN: (纸本)9798350371420;9781737749769

This article explores the integration of fully autonomous legged robots in obstacle filled environments, simultaneously addressing the challenges of navigation and control. Despite the potential of legged robots for dynamic tasks, their deployment in complex environments has been hindered by the difficulty of developing effective autonomous control systems. In particular, the motion planning problem is addressed in this article, by formulating it as a Partially Observable Markov Decision Process (POMDP) and applying Proximal Policy Optimization (PPO), a model-free Deep Reinforcement Learning (DRL) algorithm. To improve sample efficiency and real-world applicability, the proposed method incorporates a Central Pattern Generator (CPG) for motion planning and a variational autoencoder (VAE) for terrain representation, reducing the complexity of action and observation spaces. Referred to as the VAE-CPG architecture, its performance is demonstrated using the Unitree Laikago robot within the PyBullet simulation environment, aiming to show its effectiveness in simulated construction sites. Our findings indicate that by reducing the legged action space to periodic gait patterns and optimizing the gait based on sensory feedback, we achieve enhanced adaptability and efficiency. This work presents a viable means towards the deployment of autonomous legged robots and their improved efficiency in real applications.

关键词： Robotics Reinforcement Learning Computer Vision Central Pattern Generator variational autoencoder Gait optimization

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A Joint Generative Model for Zero-Shot Learning 15th

A Joint Generative Model for Zero-Shot Learning

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15th European Conference on Computer Vision (ECCV)

作者： Gao, Rui Hou, Xingsong Qin, Jie Liu, Li Zhu, Fan Zhang, Zhao Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian Peoples R China Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates Soochow Univ Suzhou Peoples R China Guangdong Xian Jiaotong Univ Acad Foshan Guangdong Peoples R China

ISBN: (纸本)9783030110185;9783030110178

Zero-shot learning (ZSL) is a challenging task due to the lack of data from unseen classes during training. Existing methods tend to have the strong bias towards seen classes, which is also known as the domain shift problem. To mitigate the gap between seen and unseen class data, we propose a joint generative model to synthesize features as the replacement for unseen data. Based on the generated features, the conventional ZSL problem can be tackled in a supervised way. Specifically, our framework integrates variational autoencoders (VAE) and Generative Adversarial Networks (GAN) conditioned on class-level semantic attributes for feature generation based on element-wise and holistic reconstruction. A categorization network acts as the additional guide to generate features beneficial for the subsequent classification task. Moreover, we propose a perceptual reconstruction loss to preserve semantic similarities. Experimental results on five benchmarks show the superiority of our framework over the state-of-the-art approaches in terms of both conventional ZSL and generalized ZSL settings.

关键词： Zero-shot learning variational autoencoder Generative adversarial network Perceptual reconstruction

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Text Generation Based on Generative Adversarial Nets with Latent Variables 22nd

Text Generation Based on Generative Adversarial Nets with La...

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22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Wang, Heng Qin, Zengchang Wan, Tao Beihang Univ Sch ASEE Intelligent Comp & Machine Learning Lab Beijing 100191 Peoples R China Beihang Univ Beijing Adv Innovat Ctr Biomed Engn Sch Biol Sci & Med Engn Beijing 100191 Peoples R China

ISBN: (纸本)9783319930374;9783319930367

In this paper, we propose a model using generative adversarial net (GAN) to generate realistic text. Instead of using standard GAN, we combine variational autoencoder (VAE) with generative adversarial net. The use of high-level latent random variables is helpful to learn the data distribution and solve the problem that generative adversarial net always emits the similar data. We propose the VGAN model where the generative model is composed of recurrent neural network and VAE. The discriminative model is a convolutional neural network. We train the model via policy gradient. We apply the proposed model to the task of text generation and compare it to other recent neural network based models, such as recurrent neural network language model and Seq-GAN. We evaluate the performance of the model by calculating negative log-likelihood and the BLEU score. We conduct experiments on three benchmark datasets, and results show that our model outperforms other previous models.

关键词： Generative adversarial net variational autoencoder VGAN Text generation

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Latent Space Visualization of Half Face and Full Face by Generative Model 15

Latent Space Visualization of Half Face and Full Face by Gen...

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15th International Conference on Quality Control by Artificial Vision

作者： Zou, Min Akashi, Takuya Iwate Univ Morioka Iwate 0208551 Japan

ISBN: (数字)9781510644274

ISBN: (纸本)9781510644274

Generally, most face detection and recognition tasks are based on the training of intact facial images and their corresponding labels. The training image is supposed to contain as much facial area as possible, and sometimes expanding the training image area to the upper body may also enhance the learning ability. However, we noticed that both the three-dimensional structure and two-dimensional appearance from the frontal view of human faces are bilaterally symmetrical. Few research makes use of this characteristics to simplify the learning process. We have proposed a flipping strategy to apply the facial symmetrical characteristic to transfer learning and proved training with half faces can also achieve equivalent performance in face recognition for a small group of individuals. This paper extend the transfer learning of cropped half face images for face recognition rather than flipping the half face. The facial symmetrical characteristics is utilized to improve face recognition through transfer learning of only a half of the common human face image. We also investigate and explain the reason why the half face area is enough to accurately classify small groups of individuals. A variational autoencoder network is utilized to impose the probability distribution on the facial latent space. Finally, the dimensions of the facial latent space are reduced to visualize the distributed perceptual manifold for face identity.

关键词： facial symmetry variational autoencoder latent space visualization facial perceptual manifold

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Self Attention in variational Sequential Learning for Summarization 20

Self Attention in Variational Sequential Learning for Summar...

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Interspeech Conference

作者： Chien, Jen-Tzung Wang, Chun-Wei Natl Chiao Tung Univ Dept Elect & Comp Engn Hsinchu Taiwan

Attention mechanism plays a crucial role in sequential learning for many speech and language applications. However, it is challenging to develop a stochastic attention in a sequence-to-sequence model which consists of two recurrent neural networks (RNNs) as the encoder and decoder. The problem of posterior collapse happens in variational inference and results in the estimated latent variables close to a standard Gaussian prior so that the information from input sequence is disregarded in learning process. This paper presents a new recurrent autoencoder for sentence representation where a self attention scheme is incorporated to activate the interaction between inference and generation in training procedure. In particular, a stochastic RNN decoder is implemented to provide additional latent variable to fulfill self attention for sentence reconstruction. The posterior collapse is alleviated. The latent information is sufficiently attended in variational sequential learning. During test phase, the estimated prior distribution of decoder is sampled for stochastic attention and generation. Experiments on Penn Treebank and Yelp 2013 show the desirable generation performance in terms of perplexity. The visualization of attention weights also illustrates the usefulness of self attention. The evaluation on DUC 2007 demonstrates the merit of variational recurrent autoencoder for document summarization.

关键词： sequence generation variational autoencoder sequence-to-sequence learning attention mechanism

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UNSUPERVISED PITCH-TIMBRE DISENTANGLEMENT OF MUSICAL INSTRUMENTS USING A JACOBIAN DISENTANGLED SEQUENTIAL autoencoder 49

UNSUPERVISED PITCH-TIMBRE DISENTANGLEMENT OF MUSICAL INSTRUM...

引用

49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Luo, Yin-Jyun Ewert, Sebastian Dixon, Simon Queen Mary Univ London Ctr Digital Music London England Spotify Stockholm Sweden

ISBN: (纸本)9798350344868;9798350344851

Disentangled representation learning seeks to align individual dimensions or separate groups of coordinates of latent factors with attributes of observed data such that perturbing certain latent factors uniquely changes particular attributes. A main challenge in unsupervised disentanglement using autoencoders is that strong regularisation, while necessary for consistent disentanglement, comes at the expense of accurate data reconstruction. To address this, we introduce a teacher-student framework that incorporates a variational sequential autoencoder and a Jacobian constraint that regularises the variation of observations relative to latent factors. In real-world audio recordings of musical instruments, our approach outperforms a state-of-the-art method in both sampling quality and unsupervised pitch-timbre disentanglement.

关键词： Disentangled representation unsupervised learning variational autoencoder music instrument

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Learning Salient Features for Speech Emotion Recognition using Attention-based Residual Bidirectional LSTM with Federated Learning 4

Learning Salient Features for Speech Emotion Recognition usi...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Kaur, Dilsheen Misra, Anuradha Vyas, O.P. Amity University Lucknow Campus Amity School of Engineering and Technology Department of Computer Science and Engineering India Indian Institute of Information Technology Allahabad Department of Information Technology India

ISBN: (纸本)9798350343670

In human communication, expressing human emotion plays an essential role in transferring information to the other individual. The expression forms of human communication are very rich, in different patterns like facial expressions, voice tone, eye contact, body language and laughter. Moreover, the languages used by individuals in the entire world are different, yet without understanding the communication language, the individuals can realize the message that other individuals require to convey as emotional expressions. Among all the human emotional expressions, the conveyed expressions through human voice are experimented with in different studies. However, identifying the emotional situation of the presenter is complicated for the machine learning techniques, which gained more attraction in Speech Emotion Recognition (SER). Here, the SER framework plays an efficient role in different real-world applications in observing human actions, virtual reality, emergency centers and robot communication to observe the emotional phase of the speakers. Thus, an innovative federated learning-based SER is presented in this paper. The essential speech signals are initially collected from the standard database. After that, the collected signal is fed to the feature extraction stage. Here, the deep features in the input signal are obtained by the variational autoencoder (VAE). Further, the deep features obtained from the VAE are passed to the "Attention-based Residual Bidirectional Long Short-Term Memory (A-RBiLSTM)"to identify the emotions in the speech signal. At last, different experimental analyses are executed to observe the effectualness of the implemented framework. © 2024 IEEE.

关键词： Attention-based Residual Bidirectional Long Short-Term Memory Deep Features Feature Extraction Federated Learning Speech Emotion Recognition variational autoencoder

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An Efficient Deep Multi-View Clustering Method of Power Users Based on a variational Automatic Encoder 5

An Efficient Deep Multi-View Clustering Method of Power User...

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5th International Conference on Clean Energy and Electric Power Engineering, ICCEPE 2024

作者： Ke, Yang Xiao, Wei Chen, Meng Feng, Yi Wu, Ze Yichang Power Supply Company State Grid Hubei Electric Power Co. Ltd. Hubei Yichang China China Three Gorges University Hubei Key Laboratory of Intelligent Vision-Based Monitoring for Hydroelectric Engineering Hubei Yichang443000 China

ISBN: (纸本)9798350390315

The electricity consumption behavior characteristics of electricity users are reflected in their electricity load data. Therefore, clustering the electricity users into groups and analyzing their characteristics can help power companies obtain a clear insight and a deeper understanding of the user groups and provide more targeted services accordingly. However, due to the large-scale and high-dimensional nature of power load data, traditional clustering methods face certain difficulties in feature extraction. In view of that, this article proposes a deep multi-view clustering method for power users based on a VAE (variational autoencoder). The proposed method first reduces the dimensionality of power load data by distributing the VAE network and then adopts a deep fusion module to achieve deep mining of the reduced data. Finally, it clusters power users using the clustering module. The experimental results demonstrate that the proposed clustering method can achieve optimal performance on both the Calinski Harabasz index and Silhouette score and a suboptimal result on the Davies Bouldin index. The results presented in this study indicate the practical effectiveness of the proposed approach and provide a solid data foundation for more accurate user behavior analysis for power companies. © 2024 IEEE.

关键词： Dual-scale similarity Electricity Load data Multi-view clustering variational autoencoder

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Enhancing Image Classification in Small and Unbalanced Datasets Through Synthetic Data Augmentation 1

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13th Workshop on Clinical Image-Based Procedures (CLIP)

作者： de la Fuente, Neil Majo, Mireia Luzko, Irina Cordova, Henry Fernandez-Esparrach, Gloria Bernal, Jorge Comp Vis Ctr Barcelona Spain Univ Autonoma Barcelona Barcelona Spain Hosp Clin Barcelona Barcelona Spain

ISBN: (数字)9783031730832

ISBN: (纸本)9783031730825;9783031730832

Accurate and robust medical image classification is a challenging task, especially in application domains where available annotated datasets are small and present high imbalance between target classes. Considering that data acquisition is not always feasible, especially for underrepresented classes, our approach introduces a novel synthetic augmentation strategy using class-specific variational autoencoders (VAEs) and latent space interpolation to improve discrimination capabilities. By generating realistic, varied synthetic data that fills feature space gaps, we address issues of data scarcity and class imbalance. The method presented in this paper relies on the interpolation of latent representations within each class, thus enriching the training set and improving the model's generalizability and diagnostic accuracy. The proposed strategy was tested in a small dataset of 321 images created to train and validate an automatic method for assessing the quality of cleanliness of esophagogastroduodenoscopy images. By combining real and synthetic data, an increase of over 18% in the accuracy of the most challenging underrepresented class was observed. The proposed strategy not only benefited the underrepresented class but also led to a general improvement in other metrics, including a 6% increase in global accuracy and precision.

关键词： Synthetic Data Augmentation variational autoencoder Esophagogastroduodenoscopy Image Classification Image Classification

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Adapting RICO dataset for boosting Graphical User Interface component classification for automated Android testing 10

Adapting RICO dataset for boosting Graphical User Interface ...

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10th International Conference on Soft Computing and Machine Intelligence (ISCMI)

作者： Lopez, Yadini Perez Albuquerque, Lais Dib de F. Costa Junior, Gilmar Joia Xavier, Daniel Lopes Ochoa, Juan David Camargo, Denizard Dimitri SIDIA Dev & Res Inst SITA AIG Automat Innovat Grp Manaus Amazonas Brazil SIDIA Dev & Res Inst SITA AIG Automat Innovat Grp Porto Velho Brazil

ISBN: (纸本)9798350359374

User Interface testing is one of the most used routines for Android feature quality verification. Regarding this, several industrial and academic solutions are available for Graphical User Interface component identification. However, the state-of-art works are still limited respecting the variety of widgets that can recognize. Also, most solutions use application metadata that can vary across different releases and device models, affecting the portability of automations. Research that approach widget classification using Machine Learning based solutions commonly use a large Android screen capture dataset called RICO. However, several annotations problems have been recurrently pointed on this dataset. In this work, we propose a selection of data cleaning and balancing techniques for removing noisy samples and leveling the number of samples per class on RICO dataset. We used a custom dataset with an extended number of classes (106) when comparing with the state-of-art works that approach widget classification with 15 classes on average. Finally, we showed that using these techniques to improve the quality of data can improve the accuracy when training Machine Learning models like Convolutional Neural Networks and eXtreme Gradient Boosting.

关键词： Android automated mobile testing variational autoencoder widget graphical user interface dataset

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