scholarly journals Comparing Class-Aware and Pairwise Loss Functions for Deep Metric Learning in Wildlife Re-Identification

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6109
Author(s):  
Nkosikhona Dlamini ◽  
Terence L. van Zyl
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Loss Function ◽  
Network Architecture ◽  
Loss Functions ◽  
Similarity Learning ◽  
Neural Network Architecture ◽  
Shot Classification ◽  
Public Datasets ◽  
The Impact

Similarity learning using deep convolutional neural networks has been applied extensively in solving computer vision problems. This attraction is supported by its success in one-shot and zero-shot classification applications. The advances in similarity learning are essential for smaller datasets or datasets in which few class labels exist per class such as wildlife re-identification. Improving the performance of similarity learning models comes with developing new sampling techniques and designing loss functions better suited to training similarity in neural networks. However, the impact of these advances is tested on larger datasets, with limited attention given to smaller imbalanced datasets such as those found in unique wildlife re-identification. To this end, we test the advances in loss functions for similarity learning on several animal re-identification tasks. We add two new public datasets, Nyala and Lions, to the challenge of animal re-identification. Our results are state of the art on all public datasets tested except Pandas. The achieved Top-1 Recall is 94.8% on the Zebra dataset, 72.3% on the Nyala dataset, 79.7% on the Chimps dataset and, on the Tiger dataset, it is 88.9%. For the Lion dataset, we set a new benchmark at 94.8%. We find that the best performing loss function across all datasets is generally the triplet loss; however, there is only a marginal improvement compared to the performance achieved by Proxy-NCA models. We demonstrate that no single neural network architecture combined with a loss function is best suited for all datasets, although VGG-11 may be the most robust first choice. Our results highlight the need for broader experimentation and exploration of loss functions and neural network architecture for the more challenging task, over classical benchmarks, of wildlife re-identification.

scholarly journals Suggestion and invention of recipes using bi-directional LSTMs-based frameworks

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
Sai Nikhil Rao Gona ◽  
Himamsu Marellapudi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Loss Function ◽  
Recurrent Neural Networks ◽  
Network Architecture ◽  
Loss Functions ◽  
Neural Network Architecture ◽  
Calorie Content ◽  
Proposed Model ◽  
Variational Autoencoder

AbstractChoosing which recipe to eat and which recipe to avoid isn’t that simple for anyone. It takes strenuous efforts and a lot of time for people to calculate the number of calories and P.H level of the dish. In this paper, we propose an ensemble neural network architecture that suggests recipes based on the taste of the person, P.H level and calorie content of the recipes. We also propose a bi-directional LSTMs-based variational autoencoder for generating new recipes. We have ensembled three bi-directional LSTM-based recurrent neural networks which can classify the recipes based on the taste of the person, P.H level of the recipe and calorie content of the recipe. The proposed model also predicts the taste ratings of the recipes for which we proposed a custom loss function which gave better results than the standard loss functions and the model also predicts the calorie content of the recipes. The bi-directional LSTMs-based variational autoencoder after being trained with the recipes which are fit for the person generates new recipes from the existing recipes. After training and testing the recurrent neural networks and the variational autoencoder, we have tested the model with 20 new recipes and got overwhelming results in the experimentation, the variational autoencoders generated a couple of new recipes, which are healthy to the specific person and will be liked by the specific person.

scholarly journals Neural Layer Bypassing Network

2021 ◽  
Author(s):  
Amogh Palasamudram
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Image Classification ◽  
Network Architecture ◽  
Classification Models ◽  
Learning Models ◽  
Neural Network Architecture ◽  
Overall Performance ◽  
The Impact

<p>This research aims to introduce and evaluate a new neural network architecture to improve the speed and effectiveness of forward propagation in neural networks: the Neural Layer Bypassing Network (NLBN). The theory and workings of this architecture have been explained in this research paper, along with comparisons to other methods of increasing the efficacy of deep learning models. This research also includes code examples with 3 image classification models trained on different datasets and analyses the impact of the NLBN architecture on forward propagation. It was found that this architecture increases the speed of forward propagation and tends to slightly decrease the accuracy of the model. However, it takes longer to train and takes more memory. All in all, this architecture is a potential foundation for using deep learning to teach deep learning models to be more efficient. This includes skipping and re-propagating through layers to improve the overall performance of a model.</p><div><br></div>

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

scholarly journals Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

2016 ◽  
Vol 807 ◽  
pp. 155-166 ◽  
Author(s):  
Julia Ling ◽  
Andrew Kurzawski ◽  
Jeremy Templeton
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Reynolds Stress ◽  
Network Architecture ◽  
Eddy Viscosity ◽  
Deep Neural Networks ◽  
Test Cases ◽  
Neural Network Architecture ◽  
Stress Anisotropy ◽  
Anisotropy Tensor

There exists significant demand for improved Reynolds-averaged Navier–Stokes (RANS) turbulence models that are informed by and can represent a richer set of turbulence physics. This paper presents a method of using deep neural networks to learn a model for the Reynolds stress anisotropy tensor from high-fidelity simulation data. A novel neural network architecture is proposed which uses a multiplicative layer with an invariant tensor basis to embed Galilean invariance into the predicted anisotropy tensor. It is demonstrated that this neural network architecture provides improved prediction accuracy compared with a generic neural network architecture that does not embed this invariance property. The Reynolds stress anisotropy predictions of this invariant neural network are propagated through to the velocity field for two test cases. For both test cases, significant improvement versus baseline RANS linear eddy viscosity and nonlinear eddy viscosity models is demonstrated.

scholarly journals Towards Heterogeneous Multi-Agent Reinforcement Learning with Graph Neural Networks

2020 ◽  
Author(s):  
Douglas Meneghetti ◽  
Reinaldo Bianchi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Communication Channels ◽  
Neural Network Architecture ◽  
Graph Representations ◽  
Labeled Graph ◽  
Multiple Agent ◽  
Multi Agent ◽  
Graph Neural Networks

This work proposes a neural network architecture that learns policies for multiple agent classes in a heterogeneous multi-agent reinforcement setting. The proposed network uses directed labeled graph representations for states, encodes feature vectors of different sizes for different entity classes, uses relational graph convolution layers to model different communication channels between entity types and learns distinct policies for different agent classes, sharing parameters wherever possible. Results have shown that specializing the communication channels between entity classes is a promising step to achieve higher performance in environments composed of heterogeneous entities.

scholarly journals Comparative Performance Analysis of Neural Network Real-Time Object Detections in Different Implementations

2020 ◽  
Vol 226 ◽  
pp. 02020
Author(s):  
Alexey V. Stadnik ◽  
Pavel S. Sazhin ◽  
Slavomir Hnatic
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computer Vision ◽  
Performance Analysis ◽  
Object Detection ◽  
Real Time ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Comparative Performance

The performance of neural networks is one of the most important topics in the field of computer vision. In this work, we analyze the speed of object detection using the well-known YOLOv3 neural network architecture in different frameworks under different hardware requirements. We obtain results, which allow us to formulate preliminary qualitative conclusions about the feasibility of various hardware scenarios to solve tasks in real-time environments.

scholarly journals Graph Self Supervised Learning: the BT, the HSIC, and the VICReg

2021 ◽  
Author(s):  
Sayan Nag
Keyword(s):  
Neural Networks ◽  
Supervised Learning ◽  
Loss Function ◽  
Data Augmentation ◽  
Learning Strategy ◽  
Loss Functions ◽  
Augmentation Strategies ◽  
Batch Sizes ◽  
Graph Neural Networks ◽  
The Impact

Self-supervised learning and pre-training strategies have developed over the last few years especially for Convolutional Neural Networks (CNNs). Recently application of such methods can also be noticed for Graph Neural Networks (GNNs). In this paper, we have used a graph based self-supervised learning strategy with different loss functions (Barlow Twins[? ], HSIC[? ], VICReg[? ]) which have shown promising results when applied with CNNs previously. We have also proposed a hybrid loss function combining the advantages of VICReg and HSIC and called it as VICRegHSIC. The performance of these aforementioned methods have been compared when applied to two different datasets namely MUTAG and PROTEINS. Moreover, the impact of different batch sizes, projector dimensions and data augmentation strategies have also been explored. The results are preliminary and we will be continuing to explore with other datasets.

scholarly journals Optimizing the Simplicial-Map Neural Network Architecture

2021 ◽  
Vol 7 (9) ◽  
pp. 173
Author(s):  
Eduardo Paluzo-Hidalgo ◽  
Rocio Gonzalez-Diaz ◽  
Miguel A. Gutiérrez-Naranjo ◽  
Jónathan Heras
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Simplicial Complexes ◽  
Original Network ◽  
Neural Network Architecture ◽  
Simplicial Map ◽  
Classification Tool ◽  
Universal Approximators

Simplicial-map neural networks are a recent neural network architecture induced by simplicial maps defined between simplicial complexes. It has been proved that simplicial-map neural networks are universal approximators and that they can be refined to be robust to adversarial attacks. In this paper, the refinement toward robustness is optimized by reducing the number of simplices (i.e., nodes) needed. We have shown experimentally that such a refined neural network is equivalent to the original network as a classification tool but requires much less storage.

scholarly journals Identifikasi Penyakit Diabetes Millitus Menggunakan Jaringan Syaraf Tiruan Dengan Metode Perambatan-Balik (Backpropagation)

2018 ◽  
Author(s):  
Sutedi Sutedi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Accuracy Rate ◽  
Layer 2 ◽  
Input Layer ◽  
Hidden Layer ◽  
Diabetes Melitus

Diabetes Melitus (DM) is dangerous disease that affect many of the variouslayer of work society. This disease is not easy to accurately recognized by thegeneral society. So we need to develop a system that can identify accurately. Systemis built using neural networks with backpropagation methods and the functionactivation sigmoid. Neural network architecture using 8 input layer, 2 output layerand 5 hidden layer. The results show that this methods succesfully clasifies datadiabetics and non diabetics with near 100% accuracy rate.

scholarly journals Baby Cry Detection in Domestic Environment using Convolutional Neural Networks

2020 ◽  
Vol 9 (7) ◽  
pp. 793-795
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Web Application ◽  
Well Being ◽  
Emotion Detection ◽  
Neural Network Architecture ◽  
Domestic Environment ◽  
The Neural Network

In this paper we will identify a cry signals of infants and the explanation behind the screams below 0-6 months of segment age. Detection of baby cry signals is essential for the pre-processing of various applications involving crial analysis for baby caregivers, such as emotion detection. Since cry signals hold baby well-being information and can be understood to an extent by experienced parents and experts. We train and validate the neural network architecture for baby cry detection and also test the fastAI with the neural network. Trained neural networks will provide a model and this model can predict the reason behind the cry sound. Only the cry sounds are recognized, and alert the user automatically. Created a web application by responding and detecting different emotions including hunger, tired, discomfort, bellypain.

Sign in / Sign up

Export Citation Format

Share Document