Self-adversarial Training and Attention for Multi-task Wheat Phenotyping

2019 ◽  
Vol 35 (6) ◽  
pp. 1009-1014 ◽  
Author(s):  
Gensheng Hu ◽  
Lidong Qian ◽  
Dong Liang ◽  
Mingzhu Wan
Keyword(s):  
Deep Learning ◽  
Precision Agriculture ◽  
Attention Mechanism ◽  
Learning Ability ◽  
Learning Networks ◽  
Data Set ◽  
Adversarial Networks ◽  
Previous State ◽  
Adversarial Training ◽  
Agriculture Management

Abstract. Phenotypic monitoring provides important data support for precision agriculture management. This study proposes a deep learning-based method to gain an accurate count of wheat ears and spikelets. The deep learning networks incorporate self-adversarial training and attention mechanism with stacked hourglass networks. Four stacked hourglass networks follow a holistic attention map to construct a generator of self-adversarial networks. The holistic attention maps enable the networks to focus on the overall consistency of the whole wheat. The discriminator of self-adversarial networks displays the same structure as the generator, which causes adversarial loss to the generator. This process improves the generator’s learning ability and prediction accuracy for occluded wheat ears. This method yields higher wheat ear count in the Annotated Crop Image Database (ACID) data set than the previous state-of-the-art algorithm. Keywords: Attention mechanism, Plant phenotype, Self-adversarial networks, Stacked hourglass.

scholarly journals Data-driven prediction of unsteady flow over a circular cylinder using deep learning

2019 ◽  
Vol 879 ◽  
pp. 217-254 ◽  
Author(s):  
Sangseung Lee ◽  
Donghyun You
Keyword(s):  
Deep Learning ◽  
Circular Cylinder ◽  
Unsteady Flow ◽  
Conservation Laws ◽  
Flow Fields ◽  
Loss Functions ◽  
Generative Adversarial Networks ◽  
Learning Networks ◽  
Adversarial Networks ◽  
Adversarial Training

Unsteady flow fields over a circular cylinder are used for training and then prediction using four different deep learning networks: generative adversarial networks with and without consideration of conservation laws; and convolutional neural networks with and without consideration of conservation laws. Flow fields at future occasions are predicted based on information on flow fields at previous occasions. Predictions of deep learning networks are made for flow fields at Reynolds numbers that were not used during training. Physical loss functions are proposed to explicitly provide information on conservation of mass and momentum to deep learning networks. An adversarial training is applied to extract features of flow dynamics in an unsupervised manner. Effects of the proposed physical loss functions and adversarial training on predicted results are analysed. Captured and missed flow physics from predictions are also analysed. Predicted flow fields using deep learning networks are in good agreement with flow fields computed by numerical simulations.

scholarly journals A Step-by-Step Guide for Automated Plant Canopy Delineation Using Deep Learning: An Example in Strawberry Using ArcGIS Pro Software

EDIS ◽  
2021 ◽  
Vol 2021 (5) ◽  
Author(s):  
Amr Abd-Elrahman ◽  
Katie Britt ◽  
Vance Whitaker
Keyword(s):  
Neural Networks ◽  
Information System ◽  
Image Analysis ◽  
Deep Learning ◽  
Geographic Information System ◽  
Precision Agriculture ◽  
System Analysis ◽  
Plant Canopy ◽  
Geographic Information System Analysis ◽  
Agriculture Management

This publication presents a guide to image analysis for researchers and farm managers who use ArcGIS software. Anyone with basic geographic information system analysis skills may follow along with the demonstration and learn to implement the Mask Region Convolutional Neural Networks model, a widely used model for object detection, to delineate strawberry canopies using ArcGIS Pro Image Analyst Extension in a simple workflow. This process is useful for precision agriculture management.

scholarly journals Multi-Turn Chatbot Based on Query-Context Attentions and Dual Wasserstein Generative Adversarial Networks

2019 ◽  
Vol 9 (18) ◽  
pp. 3908 ◽  
Author(s):  
Jintae Kim ◽  
Shinhyeok Oh ◽  
Oh-Woog Kwon ◽  
Harksoo Kim
Keyword(s):  
Performance Measures ◽  
State Of The Art ◽  
Attention Mechanism ◽  
Generative Adversarial Networks ◽  
Training Method ◽  
Adversarial Networks ◽  
Proposed Model ◽  
Previous State ◽  
Vector Representations

To generate proper responses to user queries, multi-turn chatbot models should selectively consider dialogue histories. However, previous chatbot models have simply concatenated or averaged vector representations of all previous utterances without considering contextual importance. To mitigate this problem, we propose a multi-turn chatbot model in which previous utterances participate in response generation using different weights. The proposed model calculates the contextual importance of previous utterances by using an attention mechanism. In addition, we propose a training method that uses two types of Wasserstein generative adversarial networks to improve the quality of responses. In experiments with the DailyDialog dataset, the proposed model outperformed the previous state-of-the-art models based on various performance measures.

Introducing the Deep Learning for Digital Age

2020 ◽  
pp. 317-333
Author(s):  
Shaila S. G. ◽  
Sunanda Rajkumari ◽  
Vadivel Ayyasamy
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
Language Processing ◽  
Vital Role ◽  
Learning Ability ◽  
Learning Approaches ◽  
Data Set ◽  
Use Of Data ◽  
Repetitive Learning

Deep learning is playing vital role with greater success in various applications, such as digital image processing, human-computer interaction, computer vision and natural language processing, robotics, biological applications, etc. Unlike traditional machine learning approaches, deep learning has effective ability of learning and makes better use of data set for feature extraction. Because of its repetitive learning ability, deep learning has become more popular in the present-day research works.

An end-to-End deep context gate convolutional visual odometry system based on lightweight attention mechanism

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yan Xu ◽  
Hong Qin ◽  
Jiani Huang ◽  
Yanyun Wang
Keyword(s):  
Visual Odometry ◽  
Attention Mechanism ◽  
Learning Ability ◽  
Global Features ◽  
Data Set ◽  
Content Type ◽  
Generalization Ability ◽  
The Public ◽  
Public Data ◽  
End To End

Purpose Conventional learning-based visual odometry (VO) systems usually use convolutional neural networks (CNN) to extract features, where some important context-related and attention-holding global features might be ignored. Without essential global features, VO system will be sensitive to various environmental perturbations. The purpose of this paper is to design a novel learning-based framework that aims to improve accuracy of learning-based VO without decreasing the generalization ability. Design/methodology/approach Instead of CNN, a context-gated convolution is adopted to build an end-to-end learning framework, which enables convolutional layers that dynamically capture representative local patterns and composes local features of interest under the guidance of global context. In addition, an attention mechanism module is introduced to further improve learning ability and enhance robustness and generalization ability of the VO system. Findings The proposed system is evaluated on the public data set KITTI and the self-collected data sets of our college building, where it shows competitive performance compared with some classical and state-of-the-art learning-based methods. Quantitative experimental results on the public data set KITTI show that compared with CNN-based VO methods, the average translational error and rotational error of all the test sequences are reduced by 45.63% and 37.22%, respectively. Originality/value The main contribution of this paper is that an end-to-end deep context gate convolutional VO system based on lightweight attention mechanism is proposed, which effectively improves the accuracy compared with other learning-based methods.

scholarly journals The secure steganography for hiding images via GAN

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Zhangjie Fu ◽  
Fan Wang ◽  
Xu Cheng
Keyword(s):  
Deep Learning ◽  
Color Image ◽  
Visual Quality ◽  
Learning Networks ◽  
Secret Data ◽  
Rapid Improvement ◽  
Proposed Model ◽  
Adversarial Training ◽  
Human Eyes

Abstract Steganography is one of the important methods in the field of information hiding, which is the technique of hiding secret data within an ordinary file or message in order to avoid the detection of steganalysis models and human eyes. In recent years, many scholars have applied various deep learning networks to the field of steganalysis to improve the accuracy of detection. The rapid improvement of the accuracy of steganalysis models has caused a huge threat to the security of steganography. In addition, another important factor that limits the security of steganography is capacity. The larger the capacity, the worse and more unnatural the visual quality of carrier images after embedded. Therefore, this paper proposes a steganography model—HIGAN, which constructs the encoding network composed of residual blocks to hide the color secret image into another color image of the same size to output a lower distortion and higher visual quality steganographic image. Moreover, it utilizes the adversarial training between the encoder-decoder network and the steganalysis model to improve the ability to resist the detection of steganalysis models based on deep learning. The experimental results show that our proposed model is achievable and effective. Compared with the previous steganography model for hiding color images based on deep learning, the steganography model in this article could achieve steganographic images with higher visual quality and stronger security.

scholarly journals A Strategic Analytics Using Convolutional Neural Networks for Weed Identification in Sugar Beet Fields

2021 ◽  
Vol 13 (1) ◽  
pp. 49-57
Author(s):  
Brahim Jabir ◽  
Noureddine Falih ◽  
Asmaa Sarih ◽  
Adil Tannouche
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Sugar Beet ◽  
Convolutional Neural Networks ◽  
Precision Agriculture ◽  
Large Scale ◽  
Aerial Images ◽  
Data Set ◽  
High Level ◽  
And Control

Researchers in precision agriculture regularly use deep learning that will help growers and farmers control and monitor crops during the growing season; these tools help to extract meaningful information from large-scale aerial images received from the field using several techniques in order to create a strategic analytics for making a decision. The information result of the operation could be exploited for many reasons, such as sub-plot specific weed control. Our focus in this paper is on weed identification and control in sugar beet fields, particularly the creation and optimization of a Convolutional Neural Networks model and train it according to our data set to predict and identify the most popular weed strains in the region of Beni Mellal, Morocco. All that could help select herbicides that work on the identified weeds, we explore the way of transfer learning approach to design the networks, and the famous library Tensorflow for deep learning models, and Keras which is a high-level API built on Tensorflow.

scholarly journals Enhancing the Security of Deep Learning Steganography via Adversarial Examples

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1446
Author(s):  
Yueyun Shang ◽  
Shunzhi Jiang ◽  
Dengpan Ye ◽  
Jiaqing Huang
Keyword(s):  
Deep Learning ◽  
Automatic Generation ◽  
Generative Adversarial Networks ◽  
Learning Networks ◽  
Secret Image ◽  
Adversarial Networks ◽  
Linear Behavior ◽  
Adversarial Examples ◽  
Adversarial Example

Steganography is a collection of techniques for concealing the existence of information by embedding it within a cover. With the development of deep learning, some novel steganography methods have appeared based on the autoencoder or generative adversarial networks. While the deep learning based steganography methods have the advantages of automatic generation and capacity, the security of the algorithm needs to improve. In this paper, we take advantage of the linear behavior of deep learning networks in higher space and propose a novel steganography scheme which enhances the security by adversarial example. The system is trained with different training settings on two datasets. The experiment results show that the proposed scheme could escape from deep learning steganalyzer detection. Besides, the produced stego could extract secret image with less distortion.

Generation of fashionable clothes using generative adversarial networks

2019 ◽  
Vol 32 (2) ◽  
pp. 177-187
Author(s):  
Montek Singh ◽  
Utkarsh Bajpai ◽  
Vijayarajan V. ◽  
Surya Prasath
Keyword(s):  
Deep Learning ◽  
User Interaction ◽  
Generative Adversarial Networks ◽  
Manufacturing Companies ◽  
Data Set ◽  
Content Type ◽  
Adversarial Networks ◽  
Different Types ◽  
To Come ◽  
The Impact

Purpose There are various style options available when one buys clothes on online shopping websites, however the availability the new fashion trends or choices require further user interaction in generating fashionable clothes. The paper aims to discuss this issue. Design/methodology/approach Based on generative adversarial networks (GANs) from the deep learning paradigm, here the authors suggest model system that will take the latest fashion trends and the clothes bought by users as input and generate new clothes. The new set of clothes will be based on trending fashion but at the same time will have attributes of clothes where were bought by the consumer earlier. Findings In the proposed machine learning based approach, the clothes generated by the system will personalized for different types of consumers. This will help the manufacturing companies to come up with the designs, which will directly target the customer. Research limitations/implications The biggest limitation of the collected data set is that the clothes in the two domains do not belong to a specific category. For instance the vintage clothes data set has coats, dresses, skirts, etc. These different types of clothes are not segregated. Also there is no restriction on the number of images of each type of cloth. There can many images of dresses and only a few for the coats. This can affect the end results. The aim of the paper was to find whether new and desirable clothes can be created from two different domains or not. Analyzing the impact of “the number of images for each class of cloth” is something which is aim to work in future. Practical implications The authors believe such personalized experience can increase the sales of fashion stores and here provide the feasibility of such a clothes generation system. Originality/value Applying GANs from the deep learning models for generating fashionable clothes.

scholarly journals L*a*b*Fruits: A Rapid and Robust Outdoor Fruit Detection System Combining Bio-Inspired Features with One-Stage Deep Learning Networks

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 275 ◽  
Author(s):  
Raymond Kirk ◽  
Grzegorz Cielniak ◽  
Michael Mangan
Keyword(s):  
Deep Learning ◽  
Real World ◽  
Model Complexity ◽  
Detection Accuracy ◽  
Learning Networks ◽  
Real World Data ◽  
Data Set ◽  
World Data ◽  
One Stage ◽  
Controlled Conditions

Automation of agricultural processes requires systems that can accurately detect and classify produce in real industrial environments that include variation in fruit appearance due to illumination, occlusion, seasons, weather conditions, etc. In this paper we combine a visual processing approach inspired by colour-opponent theory in humans with recent advancements in one-stage deep learning networks to accurately, rapidly and robustly detect ripe soft fruits (strawberries) in real industrial settings and using standard (RGB) camera input. The resultant system was tested on an existent data-set captured in controlled conditions as well our new real-world data-set captured on a real strawberry farm over two months. We utilise F 1 score, the harmonic mean of precision and recall, to show our system matches the state-of-the-art detection accuracy ( F 1 : 0.793 vs. 0.799) in controlled conditions; has greater generalisation and robustness to variation of spatial parameters (camera viewpoint) in the real-world data-set ( F 1 : 0.744); and at a fraction of the computational cost allowing classification at almost 30fps. We propose that the L*a*b*Fruits system addresses some of the most pressing limitations of current fruit detection systems and is well-suited to application in areas such as yield forecasting and harvesting. Beyond the target application in agriculture this work also provides a proof-of-principle whereby increased performance is achieved through analysis of the domain data, capturing features at the input level rather than simply increasing model complexity.

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