scholarly journals Inverse Feature Learning: Feature Learning Based on Representation Learning of Error

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 132937-132949
Author(s):  
Behzad Ghazanfari ◽  
Fatemeh Afghah ◽  
Mohammadtaghi Hajiaghayi
Keyword(s):  
Feature Learning ◽  
Representation Learning

scholarly journals Multi-Label Classification with Label Graph Superimposing

2020 ◽  
Vol 34 (07) ◽  
pp. 12265-12272
Author(s):  
Ya Wang ◽  
Dongliang He ◽  
Fu Li ◽  
Xiang Long ◽  
Zhichao Zhou ◽  
...  
Keyword(s):  
Recognition Performance ◽  
Rapid Development ◽  
Feature Learning ◽  
Representation Learning ◽  
Learning Technologies ◽  
Label Graph ◽  
Multiple Objects ◽  
Label System ◽  
Deep Layers ◽  
Label Correlations

Images or videos always contain multiple objects or actions. Multi-label recognition has been witnessed to achieve pretty performance attribute to the rapid development of deep learning technologies. Recently, graph convolution network (GCN) is leveraged to boost the performance of multi-label recognition. However, what is the best way for label correlation modeling and how feature learning can be improved with label system awareness are still unclear. In this paper, we propose a label graph superimposing framework to improve the conventional GCN+CNN framework developed for multi-label recognition in the following two aspects. Firstly, we model the label correlations by superimposing label graph built from statistical co-occurrence information into the graph constructed from knowledge priors of labels, and then multi-layer graph convolutions are applied on the final superimposed graph for label embedding abstraction. Secondly, we propose to leverage embedding of the whole label system for better representation learning. In detail, lateral connections between GCN and CNN are added at shallow, middle and deep layers to inject information of label system into backbone CNN for label-awareness in the feature learning process. Extensive experiments are carried out on MS-COCO and Charades datasets, showing that our proposed solution can greatly improve the recognition performance and achieves new state-of-the-art recognition performance.

scholarly journals Better and Faster: Knowledge Transfer from Multiple Self-supervised Learning Tasks via Graph Distillation for Video Classification

2018 ◽  
Author(s):  
Chenrui Zhang ◽  
Yuxin Peng
Keyword(s):  
Supervised Learning ◽  
Feature Learning ◽  
Representation Learning ◽  
Classification Task ◽  
Massive Data ◽  
Video Classification ◽  
Matching Problem ◽  
Video Representation ◽  
Teacher Student ◽  
Learning Tasks

Video representation learning is a vital problem for classification task. Recently, a promising unsupervised paradigm termed self-supervised learning has emerged, which explores inherent supervisory signals implied in massive data for feature learning via solving auxiliary tasks. However, existing methods in this regard suffer from two limitations when extended to video classification. First, they focus only on a single task, whereas ignoring complementarity among different task-specific features and thus resulting in suboptimal video representation. Second, high computational and memory cost hinders their application in real-world scenarios. In this paper, we propose a graph-based distillation framework to address these problems: (1) We propose logits graph and representation graph to transfer knowledge from multiple self-supervised tasks, where the former distills classifier-level knowledge by solving a multi-distribution joint matching problem, and the latter distills internal feature knowledge from pairwise ensembled representations with tackling the challenge of heterogeneity among different features; (2) The proposal that adopts a teacher-student framework can reduce the redundancy of knowledge learned from teachers dramatically, leading to a lighter student model that solves classification task more efficiently. Experimental results on 3 video datasets validate that our proposal not only helps learn better video representation but also compress model for faster inference.

scholarly journals Adaptive-Weighted Multiview Deep Basis Matrix Factorization for Multimedia Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Shicheng Li ◽  
Qinghua Liu ◽  
Jiangyan Dai ◽  
Wenle Wang ◽  
Xiaolin Gui ◽  
...  
Keyword(s):  
Data Analysis ◽  
Matrix Factorization ◽  
Feature Learning ◽  
Representation Learning ◽  
Feature Representation ◽  
Multimedia Data ◽  
Basis Matrix ◽  
Rich Information ◽  
Low Dimensional ◽  
Better Than

Feature representation learning is a key issue in artificial intelligence research. Multiview multimedia data can provide rich information, which makes feature representation become one of the current research hotspots in data analysis. Recently, a large number of multiview data feature representation methods have been proposed, among which matrix factorization shows the excellent performance. Therefore, we propose an adaptive-weighted multiview deep basis matrix factorization (AMDBMF) method that integrates matrix factorization, deep learning, and view fusion together. Specifically, we first perform deep basis matrix factorization on data of each view. Then, all views are integrated to complete the procedure of multiview feature learning. Finally, we propose an adaptive weighting strategy to fuse the low-dimensional features of each view so that a unified feature representation can be obtained for multiview multimedia data. We also design an iterative update algorithm to optimize the objective function and justify the convergence of the optimization algorithm through numerical experiments. We conducted clustering experiments on five multiview multimedia datasets and compare the proposed method with several excellent current methods. The experimental results demonstrate that the clustering performance of the proposed method is better than those of the other comparison methods.

scholarly journals A tutorial survey of architectures, algorithms, and applications for deep learning

2014 ◽  
Vol 3 ◽  
Author(s):  
Li Deng
Keyword(s):  
Deep Learning ◽  
Information Processing ◽  
Language Processing ◽  
Feature Learning ◽  
Representation Learning ◽  
Machine Learning Techniques ◽  
Major Work ◽  
Future Directions ◽  
Recent Developments ◽  
History Of

In this invited paper, my overview material on the same topic as presented in the plenary overview session of APSIPA-2011 and the tutorial material presented in the same conference [1] are expanded and updated to include more recent developments in deep learning. The previous and the updated materials cover both theory and applications, and analyze its future directions. The goal of this tutorial survey is to introduce the emerging area of deep learning or hierarchical learning to the APSIPA community. Deep learning refers to a class of machine learning techniques, developed largely since 2006, where many stages of non-linear information processing in hierarchical architectures are exploited for pattern classification and for feature learning. In the more recent literature, it is also connected to representation learning, which involves a hierarchy of features or concepts where higher-level concepts are defined from lower-level ones and where the same lower-level concepts help to define higher-level ones. In this tutorial survey, a brief history of deep learning research is discussed first. Then, a classificatory scheme is developed to analyze and summarize major work reported in the recent deep learning literature. Using this scheme, I provide a taxonomy-oriented survey on the existing deep architectures and algorithms in the literature, and categorize them into three classes: generative, discriminative, and hybrid. Three representative deep architectures – deep autoencoders, deep stacking networks with their generalization to the temporal domain (recurrent networks), and deep neural networks (pretrained with deep belief networks) – one in each of the three classes, are presented in more detail. Next, selected applications of deep learning are reviewed in broad areas of signal and information processing including audio/speech, image/vision, multimodality, language modeling, natural language processing, and information retrieval. Finally, future directions of deep learning are discussed and analyzed.

scholarly journals Network Representation Learning: From Traditional Feature Learning to Deep Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 205600-205617
Author(s):  
Ke Sun ◽  
Lei Wang ◽  
Bo Xu ◽  
Wenhong Zhao ◽  
Shyh Wei Teng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Feature Learning ◽  
Representation Learning ◽  
Network Representation

scholarly journals Unsupervised Learning for Concept Detection in Medical Images: A Comparative Analysis

2018 ◽  
Vol 8 (8) ◽  
pp. 1213 ◽  
Author(s):  
Eduardo Pinho ◽  
Carlos Costa
Keyword(s):  
Medical Imaging ◽  
Medical Information ◽  
Feature Learning ◽  
Representation Learning ◽  
Biomedical Literature ◽  
Medical Decision ◽  
Generative Adversarial Networks ◽  
Learning Approaches ◽  
Imaging Data ◽  
Concept Detection

As digital medical imaging becomes more prevalent and archives increase in size, representation learning exposes an interesting opportunity for enhanced medical decision support systems. On the other hand, medical imaging data is often scarce and short on annotations. In this paper, we present an assessment of unsupervised feature learning approaches for images in biomedical literature which can be applied to automatic biomedical concept detection. Six unsupervised representation learning methods were built, including traditional bags of visual words, autoencoders, and generative adversarial networks. Each model was trained, and their respective feature spaces evaluated using images from the ImageCLEF 2017 concept detection task. The highest mean F1 score of 0.108 was obtained using representations from an adversarial autoencoder, which increased to 0.111 when combined with the representations from the sparse denoising autoencoder. We conclude that it is possible to obtain more powerful representations with modern deep learning approaches than with previously popular computer vision methods. The possibility of semi-supervised learning as well as its use in medical information retrieval problems are the next steps to be strongly considered.

scholarly journals Autoencoder Regularized Network For Driving Style Representation Learning

2017 ◽  
Author(s):  
Weishan Dong ◽  
Ting Yuan ◽  
Kai Yang ◽  
Changsheng Li ◽  
Shilei Zhang
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Estimation Error ◽  
Feature Learning ◽  
Representation Learning ◽  
Identification Problem ◽  
Identification Accuracy ◽  
Unsupervised Feature Learning ◽  
Driving Style ◽  
Driver Identification

In this paper, we study learning generalized driving style representations from automobile GPS trip data. We propose a novel Autoencoder Regularized deep neural Network (ARNet) and a trip encoding framework trip2vec to learn drivers' driving styles directly from GPS records, by combining supervised and unsupervised feature learning in a unified architecture. Experiments on a challenging driver number estimation problem and the driver identification problem show that ARNet can learn a good generalized driving style representation: It significantly outperforms existing methods and alternative architectures by reaching the least estimation error on average (0.68, less than one driver) and the highest identification accuracy (by at least 3% improvement) compared with traditional supervised learning methods.

scholarly journals Frame-Guided Region-Aligned Representation for Video Person Re-Identification

2020 ◽  
Vol 34 (07) ◽  
pp. 10591-10598
Author(s):  
Zengqun Chen ◽  
Zhiheng Zhou ◽  
Junchu Huang ◽  
Pengyu Zhang ◽  
Bo Li
Keyword(s):  
Learning Strategy ◽  
Feature Learning ◽  
Representation Learning ◽  
Identification Problem ◽  
Spatial Dimensions ◽  
Benchmark Datasets ◽  
Sequence Representation ◽  
Alignment Mechanism ◽  
Temporal And Spatial ◽  
Parametric Alignment

Pedestrians in videos are usually in a moving state, resulting in serious spatial misalignment like scale variations and pose changes, which makes the video-based person re-identification problem more challenging. To address the above issue, in this paper, we propose a Frame-Guided Region-Aligned model (FGRA) for discriminative representation learning in two steps in an end-to-end manner. Firstly, based on a frame-guided feature learning strategy and a non-parametric alignment module, a novel alignment mechanism is proposed to extract well-aligned region features. Secondly, in order to form a sequence representation, an effective feature aggregation strategy that utilizes temporal alignment score and spatial attention is adopted to fuse region features in the temporal and spatial dimensions, respectively. Experiments are conducted on benchmark datasets to demonstrate the effectiveness of the proposed method to solve the misalignment problem and the superiority of the proposed method to the existing video-based person re-identification methods.

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