scholarly journals Learning with Noise: Improving Distantly-Supervised Fine-grained Entity Typing via Automatic Relabeling

2020 ◽  
Author(s):  
Haoyu Zhang ◽  
Dingkun Long ◽  
Guangwei Xu ◽  
Muhua Zhu ◽  
Pengjun Xie ◽  
...  
Keyword(s):  
Training Data ◽  
Great Success ◽  
Training Process ◽  
Fine Grained ◽  
Distant Supervision ◽  
Learning With Noise ◽  
Training Samples ◽  
Label Distribution ◽  
Noisy Labels

Fine-grained entity typing (FET) is a fundamental task for various entity-leveraging applications. Although great success has been made, existing systems still have challenges in handling noisy samples in training data introduced by distant supervision methods. To address these noise, previous studies either focus on processing the clean samples (i,e., have only one label) and noisy samples (i,e., have multiple labels) with different strategies or filtering the noisy labels based on the assumption that the distantly-supervised label set certainly contains the correct type label. In this paper, we propose a probabilistic automatic relabeling method which treats all training samples uniformly. Our method aims to estimate the pseudo-truth label distribution of each sample, and the pseudo-truth distribution will be treated as part of trainable parameters which are jointly updated during the training process. The proposed approach does not rely on any prerequisite or extra supervision, making it effective on real applications. Experiments on several benchmarks show that our method outperforms previous approaches and alleviates the noisy labeling problem.

scholarly journals Fine-Grained Named Entity Typing over Distantly Supervised Data Based on Refined Representations

2020 ◽  
Vol 34 (05) ◽  
pp. 7391-7398
Author(s):  
Muhammad Asif Ali ◽  
Yifang Sun ◽  
Bing Li ◽  
Wei Wang
Keyword(s):  
Language Processing ◽  
Training Data ◽  
Specific Context ◽  
Fine Grained ◽  
Named Entity ◽  
Distant Supervision ◽  
Proposed Model ◽  
Wide Range ◽  
Relative Score ◽  
Noisy Labels

Fine-Grained Named Entity Typing (FG-NET) is a key component in Natural Language Processing (NLP). It aims at classifying an entity mention into a wide range of entity types. Due to a large number of entity types, distant supervision is used to collect training data for this task, which noisily assigns type labels to entity mentions irrespective of the context. In order to alleviate the noisy labels, existing approaches on FG-NET analyze the entity mentions entirely independent of each other and assign type labels solely based on mention's sentence-specific context. This is inadequate for highly overlapping and/or noisy type labels as it hinders information passing across sentence boundaries. For this, we propose an edge-weighted attentive graph convolution network that refines the noisy mention representations by attending over corpus-level contextual clues prior to the end classification. Experimental evaluation shows that the proposed model outperforms the existing research by a relative score of upto 10.2% and 8.3% for macro-f1 and micro-f1 respectively.

scholarly journals Learning from Web Data Using Adversarial Discriminative Neural Networks for Fine-Grained Classification

2019 ◽  
Vol 33 ◽  
pp. 273-280 ◽  
Author(s):  
Xiaoxiao Sun ◽  
Liyi Chen ◽  
Jufeng Yang
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
State Of The Art ◽  
Training Data ◽  
Web Data ◽  
Fine Grained ◽  
Learning Framework ◽  
Attractive Option ◽  
Public Datasets ◽  
Noisy Labels

Fine-grained classification is absorbed in recognizing the subordinate categories of one field, which need a large number of labeled images, while it is expensive to label these images. Utilizing web data has been an attractive option to meet the demands of training data for convolutional neural networks (CNNs), especially when the well-labeled data is not enough. However, directly training on such easily obtained images often leads to unsatisfactory performance due to factors such as noisy labels. This has been conventionally addressed by reducing the noise level of web data. In this paper, we take a fundamentally different view and propose an adversarial discriminative loss to advocate representation coherence between standard and web data. This is further encapsulated in a simple, scalable and end-to-end trainable multi-task learning framework. We experiment on three public datasets using large-scale web data to evaluate the effectiveness and generalizability of the proposed approach. Extensive experiments demonstrate that our approach performs favorably against the state-of-the-art methods.

scholarly journals Classification and Stage Prediction of Lung Cancer using Convolutional Neural Networks

2019 ◽  
Vol 8 (10) ◽  
pp. 993-998
Keyword(s):  
Lung Cancer ◽  
State Of The Art ◽  
Early Stage ◽  
Signs And Symptoms ◽  
Large Cell ◽  
Training Data ◽  
Large Set ◽  
Fine Grained ◽  
Training Samples ◽  
Large Cell Lung Cancer

In recent years, digital image processing is widely used for the medical treatment classification and diagnosis. Lung cancer is the most leading cause of death in all over the world nowadays. Based on the signs and symptoms it can’t be diagnosis and treatment classified at the early stage. However it can be identified through the symptoms like coughing up blood and chest pain, the stages and risk factors of the cancer cannot be identified through the symptoms. The CT scanned lung images should be involved in image classification processing for earlier prediction of stages and treatment diagnosis. In existing, machine learning treatment classification can be done through the SVM classification. In case of large set of training samples, this will not be in accurate manner and it has less accuracy because of improper feature extraction techniques. Thus the performance of the classification based on the segmented features obtained in preceding sections. The extracted fine-grained training data through deep learning are utilized for the classification using Convolution Neural Network (CNN). In this paper, we propose a novel framework to classify both small cell and large cell lung cancer and predict its type and treatment using CNN. It is also concentrates on the preprocessing and segmentation processes to accomplish the accuracy in prediction. The experiment results in Python - TensorFlow with Kaggle image dataset show that compared to state of the art of classification and prediction methods, the proposed scheme can obtain much higher accuracy in type prediction and treatment diagnosis.

scholarly journals Siamese Reconstruction Network: Accurate Image Reconstruction from Human Brain Activity by Learning to Compare

2019 ◽  
Vol 9 (22) ◽  
pp. 4749
Author(s):  
Lingyun Jiang ◽  
Kai Qiao ◽  
Linyuan Wang ◽  
Chi Zhang ◽  
Jian Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Human Brain ◽  
Brain Activity ◽  
Feature Space ◽  
Training Data ◽  
Reconstruction Method ◽  
Learning Method ◽  
Training Samples ◽  
Visual Reconstruction ◽  
Relationship Of

Decoding human brain activities, especially reconstructing human visual stimuli via functional magnetic resonance imaging (fMRI), has gained increasing attention in recent years. However, the high dimensionality and small quantity of fMRI data impose restrictions on satisfactory reconstruction, especially for the reconstruction method with deep learning requiring huge amounts of labelled samples. When compared with the deep learning method, humans can recognize a new image because our human visual system is naturally capable of extracting features from any object and comparing them. Inspired by this visual mechanism, we introduced the mechanism of comparison into deep learning method to realize better visual reconstruction by making full use of each sample and the relationship of the sample pair by learning to compare. In this way, we proposed a Siamese reconstruction network (SRN) method. By using the SRN, we improved upon the satisfying results on two fMRI recording datasets, providing 72.5% accuracy on the digit dataset and 44.6% accuracy on the character dataset. Essentially, this manner can increase the training data about from n samples to 2n sample pairs, which takes full advantage of the limited quantity of training samples. The SRN learns to converge sample pairs of the same class or disperse sample pairs of different class in feature space.

scholarly journals Domain randomization-enhanced deep learning models for bird detection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xin Mao ◽  
Jun Kang Chow ◽  
Pin Siang Tan ◽  
Kuan-fu Liu ◽  
Jimmy Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Continuous Monitoring ◽  
Bird Species ◽  
Training Data ◽  
Learning Models ◽  
Fine Grained ◽  
Bird Detection ◽  
Relationship Of ◽  
The Relationship

AbstractAutomatic bird detection in ornithological analyses is limited by the accuracy of existing models, due to the lack of training data and the difficulties in extracting the fine-grained features required to distinguish bird species. Here we apply the domain randomization strategy to enhance the accuracy of the deep learning models in bird detection. Trained with virtual birds of sufficient variations in different environments, the model tends to focus on the fine-grained features of birds and achieves higher accuracies. Based on the 100 terabytes of 2-month continuous monitoring data of egrets, our results cover the findings using conventional manual observations, e.g., vertical stratification of egrets according to body size, and also open up opportunities of long-term bird surveys requiring intensive monitoring that is impractical using conventional methods, e.g., the weather influences on egrets, and the relationship of the migration schedules between the great egrets and little egrets.

scholarly journals BeautyNet: Joint Multiscale CNN and Transfer Learning Method for Unconstrained Facial Beauty Prediction

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yikui Zhai ◽  
He Cao ◽  
Wenbo Deng ◽  
Junying Gan ◽  
Vincenzo Piuri ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Classification Accuracy ◽  
Learning Strategy ◽  
State Of The Art ◽  
Activation Function ◽  
Training Data ◽  
Fine Grained ◽  
Pattern Recognition Problem ◽  
Face Features ◽  
Facial Beauty

Because of the lack of discriminative face representations and scarcity of labeled training data, facial beauty prediction (FBP), which aims at assessing facial attractiveness automatically, has become a challenging pattern recognition problem. Inspired by recent promising work on fine-grained image classification using the multiscale architecture to extend the diversity of deep features, BeautyNet for unconstrained facial beauty prediction is proposed in this paper. Firstly, a multiscale network is adopted to improve the discriminative of face features. Secondly, to alleviate the computational burden of the multiscale architecture, MFM (max-feature-map) is utilized as an activation function which can not only lighten the network and speed network convergence but also benefit the performance. Finally, transfer learning strategy is introduced here to mitigate the overfitting phenomenon which is caused by the scarcity of labeled facial beauty samples and improves the proposed BeautyNet’s performance. Extensive experiments performed on LSFBD demonstrate that the proposed scheme outperforms the state-of-the-art methods, which can achieve 67.48% classification accuracy.

Prediction Models for Truck Accidents at Freeway Ramps in Washington State Using Regression and Artificial Intelligence Techniques

1998 ◽  
Vol 1635 (1) ◽  
pp. 30-36 ◽  
Author(s):  
Wael H. Awad ◽  
Bruce N. Janson
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Linear Regression ◽  
Prediction Models ◽  
Washington State ◽  
Training Data ◽  
Coefficient Of Determination ◽  
Training Process ◽  
Truck Accidents ◽  
High Level

Three different modeling approaches were applied to explain truck accidents at interchanges in Washington State during a 27-month period. Three models were developed for each ramp type including linear regression, neural networks, and a hybrid system using fuzzy logic and neural networks. The study showed that linear regression was able to predict accident frequencies that fell within one standard deviation from the overall mean of the dependent variable. However, the coefficient of determination was very low in all cases. The other two artificial intelligence (AI) approaches showed a high level of performance in identifying different patterns of accidents in the training data and presented a better fit when compared to the regression model. However, the ability of these AI models to predict test data that were not included in the training process showed unsatisfactory results.

scholarly journals Applying a Cerebellar Model Articulation Controller Neural Network to a Photovoltaic Power Generation System Fault Diagnosis

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Kuei-Hsiang Chao ◽  
Bo-Jyun Liao ◽  
Chin-Pao Hung
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Fault Diagnosis ◽  
Cerebellar Model Articulation Controller ◽  
Training Process ◽  
Cmac Neural Network ◽  
Photovoltaic Power ◽  
Training Samples ◽  
Parallel Connections ◽  
Model Features

This study employed a cerebellar model articulation controller (CMAC) neural network to conduct fault diagnoses on photovoltaic power generation systems. We composed a module array using 9 series and 2 parallel connections of SHARP NT-R5E3E 175 W photovoltaic modules. In addition, we used data that were outputted under various fault conditions as the training samples for the CMAC and used this model to conduct the module array fault diagnosis after completing the training. The results of the training process and simulations indicate that the method proposed in this study requires fewer number of training times compared to other methods. In addition to significantly increasing the accuracy rate of the fault diagnosis, this model features a short training duration because the training process only tunes the weights of the exited memory addresses. Therefore, the fault diagnosis is rapid, and the detection tolerance of the diagnosis system is enhanced.

GaitSense: Towards Ubiquitous Gait-Based Human Identification with Wi-Fi

2022 ◽  
Vol 18 (1) ◽  
pp. 1-24
Author(s):  
Yi Zhang ◽  
Yue Zheng ◽  
Guidong Zhang ◽  
Kun Qian ◽  
Chen Qian ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Gait Recognition ◽  
Wearable Sensors ◽  
Human Identification ◽  
Training Data ◽  
Identification Accuracy ◽  
Identification System ◽  
Gait Patterns ◽  
Training Samples ◽  
Augmentation Techniques

Gait, the walking manner of a person, has been perceived as a physical and behavioral trait for human identification. Compared with cameras and wearable sensors, Wi-Fi-based gait recognition is more attractive because Wi-Fi infrastructure is almost available everywhere and is able to sense passively without the requirement of on-body devices. However, existing Wi-Fi sensing approaches impose strong assumptions of fixed user walking trajectories, sufficient training data, and identification of already known users. In this article, we present GaitSense , a Wi-Fi-based human identification system, to overcome the above unrealistic assumptions. To deal with various walking trajectories and speeds, GaitSense first extracts target specific features that best characterize gait patterns and applies novel normalization algorithms to eliminate gait irrelevant perturbation in signals. On this basis, GaitSense reduces the training efforts in new deployment scenarios by transfer learning and data augmentation techniques. GaitSense also enables a distinct feature of illegal user identification by anomaly detection, making the system readily available for real-world deployment. Our implementation and evaluation with commodity Wi-Fi devices demonstrate a consistent identification accuracy across various deployment scenarios with little training samples, pushing the limit of gait recognition with Wi-Fi signals.

New active learning algorithms for near-infrared spectroscopy in agricultural applications

2021 ◽  
Vol 69 (4) ◽  
pp. 297-306
Author(s):  
Julius Krause ◽  
Maurice Günder ◽  
Daniel Schulz ◽  
Robin Gruna
Keyword(s):  
Active Learning ◽  
Near Infrared ◽  
Agricultural Products ◽  
Training Data ◽  
Calibration Model ◽  
Learning Approaches ◽  
Training Samples ◽  
Agricultural Applications ◽  
Selection Of

Abstract The selection of training data determines the quality of a chemometric calibration model. In order to cover the entire parameter space of known influencing parameters, an experimental design is usually created. Nevertheless, even with a carefully prepared Design of Experiment (DoE), redundant reference analyses are often performed during the analysis of agricultural products. Because the number of possible reference analyses is usually very limited, the presented active learning approaches are intended to provide a tool for better selection of training samples.

Sign in / Sign up

Export Citation Format

Share Document