HD-MTL: Hierarchical Deep Multi-Task Learning for Large-Scale Visual Recognition

As more and more sensor data have been collected, automated detection, and diagnosis systems are urgently needed to lessen the increasing monitoring burden and reduce the risk of system faults. A plethora of researches have been done on anomaly detection, event detection, anomaly diagnosis respectively. However, none of current approaches can explore all these respects in one unified framework. In this work, a Multi-Task Learning based Encoder-Decoder (MTLED) which can simultaneously detect anomalies, diagnose anomalies, and detect events is proposed. In MTLED, feature matrix is introduced so that features are extracted for each time point and point-wise anomaly detection can be realized in an end-to-end way. Anomaly diagnosis and event detection share the same feature matrix with anomaly detection in the multi-task learning framework and also provide important information for system monitoring. To train such a comprehensive detection and diagnosis system, a large-scale multivariate time series dataset which contains anomalies of multiple types is generated with simulation tools. Extensive experiments on the synthetic dataset verify the effectiveness of MTLED and its multi-task learning framework, and the evaluation on a real-world dataset demonstrates that MTLED can be used in other application scenarios through transfer learning.

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Sparse Output Coding for Large-Scale Visual Recognition

2013 IEEE Conference on Computer Vision and Pattern Recognition ◽

10.1109/cvpr.2013.430 ◽

2013 ◽

Cited By ~ 12

Author(s):

Bin Zhao ◽

Eric P. Xing

Keyword(s):

Large Scale ◽

Visual Recognition ◽

Output Coding

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Reliability of a Visual Recognition System for Detection of Johnsongrass(Sorghum halepense) in Corn

Weed Technology ◽

10.1614/wt-d-10-00082.1 ◽

2011 ◽

Vol 25 (4) ◽

pp. 645-651 ◽

Cited By ~ 7

Author(s):

Dionisio Andújar ◽

Ángela Ribeiro ◽

Cesar Fernández-Quintanilla ◽

José Dorado

Keyword(s):

Large Scale ◽

Visual Recognition ◽

Visual Detection ◽

Cropping Systems ◽

Experimental Period ◽

Recognition System ◽

Infested Area ◽

Net Returns ◽

Cropping Season ◽

Combine Harvester

The feasibility of visual detection of weeds for map-based patch spraying systems needs to be assessed for use in large-scale cropping systems. The main objective of this research was to evaluate the reliability and profitability of using maps of Johnsongrass patches constructed at harvest to predict spatial distribution of weeds during the next cropping season. Johnsongrass patches visually were assessed from the cabin of a combine harvester in three corn fields and were compared with maps obtained in the subsequent year prior to postemergence herbicide application. There was a good correlation (71% on average) between the position of Johnsongrass patches on the two maps (fall vs. spring). The highest correlation (82%) was obtained with relatively large infestations, whereas the lowest (58%) was obtained when the infested area was smaller. Although the relative positions of the patches remained almost unchanged from 1 yr to the next, the infested area increased in all fields during the 4-yr experimental period. According to our estimates, using a strategy based on spraying full rates of herbicides to patches recorded in the map generated in the previous fall resulted in higher net returns than spraying the whole field, either at full or half rate. This site-specific strategy resulted in an average 65% reduction in the volume of herbicide applied to control this weed.

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Deep hashing with multi-task learning for large-scale instance-level vehicle search

2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW) ◽

10.1109/icmew.2017.8026274 ◽

2017 ◽

Cited By ~ 1

Author(s):

Dawei Liang ◽

Ke Yan ◽

Yaowei Wang ◽

Wei Zeng ◽

Qingsheng Yuan ◽

...

Keyword(s):

Large Scale ◽

Task Learning ◽

Deep Hashing

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Deep Large-Scale Multi-task Learning Network for Gene Expression Inference

Lecture Notes in Computer Science - Research in Computational Molecular Biology ◽

10.1007/978-3-030-45257-5_2 ◽

2020 ◽

pp. 19-36 ◽

Cited By ~ 1

Author(s):

Kamran Ghasedi Dizaji ◽

Wei Chen ◽

Heng Huang

Keyword(s):

Gene Expression ◽

Large Scale ◽

Learning Network ◽

Task Learning

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Image Classification Using Transfer Learning and Deep Learning

International Journal Of Engineering And Computer Science ◽

10.18535/ijecs/v10i9.4622 ◽

2021 ◽

Vol 10 (9) ◽

pp. 25394-25398

Author(s):

Chitra Desai

Keyword(s):

Neural Network ◽

Deep Learning ◽

Image Classification ◽

Transfer Learning ◽

Network Model ◽

Neural Network Model ◽

Large Scale ◽

Deep Neural Network ◽

Visual Recognition ◽

Classification Of Images

Deep learning models have demonstrated improved efficacy in image classification since the ImageNet Large Scale Visual Recognition Challenge started since 2010. Classification of images has further augmented in the field of computer vision with the dawn of transfer learning. To train a model on huge dataset demands huge computational resources and add a lot of cost to learning. Transfer learning allows to reduce on cost of learning and also help avoid reinventing the wheel. There are several pretrained models like VGG16, VGG19, ResNet50, Inceptionv3, EfficientNet etc which are widely used. This paper demonstrates image classification using pretrained deep neural network model VGG16 which is trained on images from ImageNet dataset. After obtaining the convolutional base model, a new deep neural network model is built on top of it for image classification based on fully connected network. This classifier will use features extracted from the convolutional base model.

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Multi-modal transportation recommendation with unified route representation learning

Proceedings of the VLDB Endowment ◽

10.14778/3430915.3430924 ◽

2020 ◽

Vol 14 (3) ◽

pp. 342-350

Author(s):

Hao Liu ◽

Jindong Han ◽

Yanjie Fu ◽

Jingbo Zhou ◽

Xinjiang Lu ◽

...

Keyword(s):

Large Scale ◽

Transportation Networks ◽

Representation Learning ◽

Transportation Systems ◽

Graph Representation ◽

Dynamic Graph ◽

Arbitrary Length ◽

Task Learning ◽

Semantic Coherence ◽

Spatio Temporal

Multi-modal transportation recommendation aims to provide the most appropriate travel route with various transportation modes according to certain criteria. After analyzing large-scale navigation data, we find that route representations exhibit two patterns: spatio-temporal autocorrelations within transportation networks and the semantic coherence of route sequences. However, there are few studies that consider both patterns when developing multi-modal transportation systems. To this end, in this paper, we study multi-modal transportation recommendation with unified route representation learning by exploiting both spatio-temporal dependencies in transportation networks and the semantic coherence of historical routes. Specifically, we propose to unify both dynamic graph representation learning and hierarchical multi-task learning for multi-modal transportation recommendations. Along this line, we first transform the multi-modal transportation network into time-dependent multi-view transportation graphs and propose a spatiotemporal graph neural network module to capture the spatial and temporal autocorrelation. Then, we introduce a coherent-aware attentive route representation learning module to project arbitrary-length routes into fixed-length representation vectors, with explicit modeling of route coherence from historical routes. Moreover, we develop a hierarchical multi-task learning module to differentiate route representations for different transport modes, and this is guided by the final recommendation feedback as well as multiple auxiliary tasks equipped in different network layers. Extensive experimental results on two large-scale real-world datasets demonstrate the performance of the proposed system outperforms eight baselines.

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Evaluation of Pre-Trained Convolutional Neural Network Models for Object Recognition

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.15.17509 ◽

2018 ◽

Vol 7 (3.15) ◽

pp. 95 ◽

Cited By ~ 1

Author(s):

M Zabir ◽

N Fazira ◽

Zaidah Ibrahim ◽

Nurbaity Sabri

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Large Scale ◽

Visual Recognition ◽

Error Function ◽

Network Models ◽

Neural Network Models ◽

California Institute Of Technology ◽

Institute Of Technology ◽

Similar Accuracy

This paper aims to evaluate the accuracy performance of pre-trained Convolutional Neural Network (CNN) models, namely AlexNet and GoogLeNet accompanied by one custom CNN. AlexNet and GoogLeNet have been proven for their good capabilities as these network models had entered ImageNet Large Scale Visual Recognition Challenge (ILSVRC) and produce relatively good results. The evaluation results in this research are based on the accuracy, loss and time taken of the training and validation processes. The dataset used is Caltech101 by California Institute of Technology (Caltech) that contains 101 object categories. The result reveals that custom CNN architecture produces 91.05% accuracy whereas AlexNet and GoogLeNet achieve similar accuracy which is 99.65%. GoogLeNet consistency arrives at an early training stage and provides minimum error function compared to the other two models.

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