scholarly journals Towards Interpretable Semantic Segmentation via Gradient-Weighted Class Activation Mapping (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13943-13944
Author(s):  
Kira Vinogradova ◽  
Alexandr Dibrov ◽  
Gene Myers
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Wide Range ◽  
Gradient Based ◽  
Activation Mapping

Convolutional neural networks have become state-of-the-art in a wide range of image recognition tasks. The interpretation of their predictions, however, is an active area of research. Whereas various interpretation methods have been suggested for image classification, the interpretation of image segmentation still remains largely unexplored. To that end, we propose seg-grad-cam, a gradient-based method for interpreting semantic segmentation. Our method is an extension of the widely-used Grad-CAM method, applied locally to produce heatmaps showing the relevance of individual pixels for semantic segmentation.

scholarly journals Image Classification for the Automatic Feature Extraction in Human Worn Fashion Data

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 624
Author(s):  
Stefan Rohrmanstorfer ◽  
Mikhail Komarov ◽  
Felix Mödritscher
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Image Data ◽  
Classification Model ◽  
Upper Body ◽  
Automatic Feature Extraction

With the always increasing amount of image data, it has become a necessity to automatically look for and process information in these images. As fashion is captured in images, the fashion sector provides the perfect foundation to be supported by the integration of a service or application that is built on an image classification model. In this article, the state of the art for image classification is analyzed and discussed. Based on the elaborated knowledge, four different approaches will be implemented to successfully extract features out of fashion data. For this purpose, a human-worn fashion dataset with 2567 images was created, but it was significantly enlarged by the performed image operations. The results show that convolutional neural networks are the undisputed standard for classifying images, and that TensorFlow is the best library to build them. Moreover, through the introduction of dropout layers, data augmentation and transfer learning, model overfitting was successfully prevented, and it was possible to incrementally improve the validation accuracy of the created dataset from an initial 69% to a final validation accuracy of 84%. More distinct apparel like trousers, shoes and hats were better classified than other upper body clothes.

scholarly journals Semantic Image Segmentation with Deep Convolutional Neural Networks and Quick Shift

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 427 ◽  
Author(s):  
Sanxing Zhang ◽  
Zhenhuan Ma ◽  
Gang Zhang ◽  
Tao Lei ◽  
Rui Zhang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Convolutional Neural Networks ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Input Image ◽  
Feature Representation ◽  
Segmentation Algorithm ◽  
Deep Convolutional Neural Networks ◽  
Semantic Image Segmentation

Semantic image segmentation, as one of the most popular tasks in computer vision, has been widely used in autonomous driving, robotics and other fields. Currently, deep convolutional neural networks (DCNNs) are driving major advances in semantic segmentation due to their powerful feature representation. However, DCNNs extract high-level feature representations by strided convolution, which makes it impossible to segment foreground objects precisely, especially when locating object boundaries. This paper presents a novel semantic segmentation algorithm with DeepLab v3+ and super-pixel segmentation algorithm-quick shift. DeepLab v3+ is employed to generate a class-indexed score map for the input image. Quick shift is applied to segment the input image into superpixels. Outputs of them are then fed into a class voting module to refine the semantic segmentation results. Extensive experiments on proposed semantic image segmentation are performed over PASCAL VOC 2012 dataset, and results that the proposed method can provide a more efficient solution.

scholarly journals Friendly Farmer

2021 ◽  
pp. 488-491
Author(s):  
Ritwik Chavhan ◽  
Kadir Sheikh ◽  
Rishikesh Bondade ◽  
Swaraj Dhanulkar ◽  
Aniket Ninave ◽  
...  
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Food Security ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Associate Degree ◽  
Plant Disease ◽  
State Of The Art ◽  
Smallholder Farmers ◽  
Definite Diagnosis

Plant disease is an ongoing challenge for smallholder farmers, which threatens income and food security. The recent revolution in smartphone penetration and computer vision models has created an opportunity for image classification in agriculture. The project focuses on providing the data relating to the pesticide/insecticide and therefore the quantity of pesticide/insecticide to be used for associate degree unhealthy crop. The user, is that the farmer clicks an image of the crop and uploads it to the server via the humanoid application. When uploading the image the farmer gets associate degree distinctive ID displayed on his application screen. The farmer must create note of that ID since that ID must be utilized by the farmer later to retrieve the message when a minute. The uploaded image is then processed by Convolutional Neural Networks. Convolutional Neural Networks (CNNs) are considered state-of-the-art in image recognition and offer the ability to provide a prompt and definite diagnosis. Then the result consisting of the malady name and therefore the affected space is retrieved. This result's then uploaded into the message table within the server. Currently the Farmer are going to be ready to retrieve the whole info during a respectable format by coming into the distinctive ID he had received within the Application.

scholarly journals OPTIMAL UNIFORM QUANTIZATION OF PARAMETERS OF CONVOLUTIONAL NEURAL NETWORKS

2018 ◽  
pp. 99-103
Author(s):  
D. S. Kolesnikov ◽  
D. A. Kuznetsov
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Mobile Applications ◽  
Recognition Accuracy ◽  
State Of The Art ◽  
Network Parameters ◽  
Wide Range ◽  
Uniform Quantization ◽  
Adaptive Step

State of the art convolutional neural networks provide high accuracy in solving a wide range of problems. Usually it is achieved by a significant increasing their computational complexity and the representation of the network parameters in single-precision floating point numbers. However, due to the limited resources, the application of networks in embedded systems and mobile applications in real time is problematic. One of the methods to solve this problem is to reduce the bit depth of data and use integer arithmetic. For this purpose, the network parameters are quantized. Performing quantization, it is necessary to ensure a minimum loss of recognition accuracy. The article proposes to use an optimal uniform quantizer with an adaptive step. The quantizer step depends on the distribution function of the quantized parameters. It reduces the effect of the quantization error on the recognition accuracy. There are also described approaches to improving the quality of quantization. The proposed quantization method is estimated on the CIFAR-10 database. It is shown that the optimal uniform quantizer for CIFAR-10 database with 8-bit representation of network parameters allows to achieve the accuracy of the initial trained network.

Sanitizing hidden activations for improving adversarial robustness of convolutional neural networks

2021 ◽  
pp. 1-11
Author(s):  
Tianshi Mu ◽  
Kequan Lin ◽  
Huabing Zhang ◽  
Jian Wang
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
State Of The Art ◽  
Black Box ◽  
Experimental Results ◽  
Amplification Effect ◽  
Wide Range ◽  
Adversarial Examples

Deep learning is gaining significant traction in a wide range of areas. Whereas, recent studies have demonstrated that deep learning exhibits the fatal weakness on adversarial examples. Due to the black-box nature and un-transparency problem of deep learning, it is difficult to explain the reason for the existence of adversarial examples and also hard to defend against them. This study focuses on improving the adversarial robustness of convolutional neural networks. We first explore how adversarial examples behave inside the network through visualization. We find that adversarial examples produce perturbations in hidden activations, which forms an amplification effect to fool the network. Motivated by this observation, we propose an approach, termed as sanitizing hidden activations, to help the network correctly recognize adversarial examples by eliminating or reducing the perturbations in hidden activations. To demonstrate the effectiveness of our approach, we conduct experiments on three widely used datasets: MNIST, CIFAR-10 and ImageNet, and also compare with state-of-the-art defense techniques. The experimental results show that our sanitizing approach is more generalized to defend against different kinds of attacks and can effectively improve the adversarial robustness of convolutional neural networks.

scholarly journals Artist, Style and Year Classification using Face Recognition and Clustering with Convolutional Neural Networks

2020 ◽  
Author(s):  
Doruk Pancaroglu
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Face Recognition ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Performance Metrics ◽  
Fine Art ◽  
Classification Methods

Artist, year and style classification of fine-art paintings are generally achieved using standard image classification methods, image segmentation, or more recently, convolutional neural networks (CNNs). This works aims to use newly developed face recognition methods such as FaceNet that use CNNs to cluster fine-art paintings using the extracted faces in the paintings, which are found abundantly. A dataset consisting of over 80,000 paintings from over 1000 artists is chosen, and three separate face recognition and clustering tasks are performed. The produced clusters are analyzed by the file names of the paintings and the clusters are named by their majority artist, year range, and style. The clusters are further analyzed and their performance metrics are calculated. The study shows promising results as the artist, year, and styles are clustered with an accuracy of 58.8, 63.7, and 81.3 percent, while the clusters have an average purity of 63.1, 72.4, and 85.9 percent.

scholarly journals Direct Quantization for Training Highly Accurate Low Bit-width Deep Neural Networks

2020 ◽  
Author(s):  
Tuan Hoang ◽  
Thanh-Toan Do ◽  
Tam V. Nguyen ◽  
Ngai-Man Cheung
Keyword(s):  
Neural Networks ◽  
Cost Function ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Gradient Descent ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Deep Convolutional Neural Networks ◽  
Novel Method ◽  
The Cost

This paper proposes two novel techniques to train deep convolutional neural networks with low bit-width weights and activations. First, to obtain low bit-width weights, most existing methods obtain the quantized weights by performing quantization on the full-precision network weights. However, this approach would result in some mismatch: the gradient descent updates full-precision weights, but it does not update the quantized weights. To address this issue, we propose a novel method that enables direct updating of quantized weights with learnable quantization levels to minimize the cost function using gradient descent. Second, to obtain low bit-width activations, existing works consider all channels equally. However, the activation quantizers could be biased toward a few channels with high-variance. To address this issue, we propose a method to take into account the quantization errors of individual channels. With this approach, we can learn activation quantizers that minimize the quantization errors in the majority of channels. Experimental results demonstrate that our proposed method achieves state-of-the-art performance on the image classification task, using AlexNet, ResNet and MobileNetV2 architectures on CIFAR-100 and ImageNet datasets.

Convolutional neural networks as aid in core lithofacies classification

2019 ◽  
Vol 7 (3) ◽  
pp. SF27-SF40 ◽  
Author(s):  
Rafael Pires de Lima ◽  
Fnu Suriamin ◽  
Kurt J. Marfurt ◽  
Matthew J. Pranter
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Data Science ◽  
Science Studies ◽  
Deep Convolutional Neural Networks ◽  
Wide Range ◽  
Description Task ◽  
Self Driving Cars

Artificial intelligence methods have a very wide range of applications. From speech recognition to self-driving cars, the development of modern deep-learning architectures is helping researchers to achieve new levels of accuracy in different fields. Although deep convolutional neural networks (CNNs) (a kind of deep-learning technique) have reached or surpassed human-level performance in image recognition tasks, little has been done to transport this new image classification technology to geoscientific problems. We have developed what we believe to be the first use of CNNs to identify lithofacies in cores. We use highly accurate models (trained with millions of images) and transfer learning to classify images of cored carbonate rocks. We found that different modern CNN architectures can achieve high levels of lithologic image classification accuracy (approximately 90%) and can aid in the core description task. This core image classification technique has the potential to greatly standardize and accelerate the description process. We also provide the community with a new set of labeled data that can be used for further geologic/data science studies.

Fruit Image Classification Using Convolutional Neural Networks

2019 ◽  
Vol 7 (4) ◽  
pp. 51-70
Author(s):  
Shawon Ashraf ◽  
Ivan Kadery ◽  
Md Abdul Ahad Chowdhury ◽  
Tahsin Zahin Mahbub ◽  
Rashedur M. Rahman
Keyword(s):  
Neural Networks ◽  
Image Classification ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Input Image ◽  
Initial Model ◽  
Image Dataset ◽  
Human Inspection

Convolutional neural networks (CNN) are the most popular class of models for image recognition and classification task nowadays. Most of the superstores and fruit vendors resort to human inspection to check the quality of the fruits stored in their inventory. However, this process can be automated. We propose a system that can be trained with a fruit image dataset and then detect whether a fruit is rotten or fresh from an input image. We built the initial model using the Inception V3 model and trained with our dataset applying transfer learning.

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