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检索条件"任意字段=Conference on Applications of Artificial Neural Networks in Image Processing II"

共 2642 条记录，以下是301-310 订阅

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Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted networks 37

Efficient Bayesian Learning Curve Extrapolation using Prior-...

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37th conference on neural Information processing Systems (NeurIPS)

作者： Adriaensen, Steven Rakotoarison, Herilalaina Mueller, Samuel Hutter, Frank Univ Freiburg Machine Learning Lab Freiburg Germany

ISBN: (纸本)9781713899921

Learning curve extrapolation aims to predict model performance in later epochs of training, based on the performance in earlier epochs. In this work, we argue that, while the inherent uncertainty in the extrapolation of learning curves warrants a Bayesian approach, existing methods are (i) overly restrictive, and/or (ii) computationally expensive. We describe the first application of prior-data fitted neural networks (PFNs) in this context. A PFN is a transformer, pre-trained on data generated from a prior, to perform approximate Bayesian inference in a single forward pass. We propose LC-PFN, a PFN trained to extrapolate artificial right-censored learning curves generated from a parametric prior proposed in prior art using MCMC. We demonstrate that LC-PFN can approximate the posterior predictive distribution over learning curves more accurately than MCMC, while being over 10 000 times faster. We also show that the same LC-PFN achieves competitive performance extrapolating a total of 20 000 real learning curves from four learning curve benchmarks (LCBench, NAS-Bench-201, Taskset, and PD1) that stem from training a wide range of model architectures (MLPs, CNNs, RNNs, and Transformers) on 53 different datasets with varying input modalities (tabular, image, text, and protein data). Finally, we investigate its potential in the context of model selection and find that a simple LC-PFN based predictive early stopping criterion obtains 2 - 6x speed-ups on 45 of these datasets, at virtually no overhead.

关键词： Extrapolation

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An Exploration of the Impact of Training Datasets on Deep Learning-Based Building Extraction 6

An Exploration of the Impact of Training Datasets on Deep Le...

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6th International conference on Internet of Things, Automation and artificial Intelligence, IoTAAI 2024

作者： Yang, Shengcheng Song, Chengshun Bao, Liang Zhao, Guangde China Construction Third Engineering Bureau Group Co. LTD Technical Department Xining China

ISBN: (纸本)9798350386974

Building extraction is important in several applications such as urban planning, disaster assessment and navigation systems, and helps to improve the accuracy and application efficiency of spatial data. In recent years, deep learning techniques have progressed significantly in the field of building extraction. However, the quality and diversity of the training dataset are crucial to the model performance. This study aims to investigate the impact of training datasets on deep learning-based building extraction. By comparing datasets of different sizes, quality, and diversity, we evaluate their performance on a deep learning model (UNet) in a building extraction task. The experimental results show that a high-quality and diverse training dataset significantly improves the extraction accuracy and robustness of the model. The deep learning model trained with the richer dataset achieves an overall accuracy of 96.5% for building extraction, which is a 13% improvement in extraction accuracy and a 10% improvement in IoU compared to the model trained on the other dataset. Meanwhile, we found that data enhancement techniques (e.g., image rotation, flipping, and numerical stretching) help improve the generalization ability of the model. The results in this paper not only provide valuable insights into the building extraction task, but also provide methodological guidance for constructing datasets for future deep learning-based remote sensing image processing research. © 2024 IEEE.

关键词： Convolutional neural networks

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Efficient Monkeypox Detection in Skin Lesions Using Pre-Trained Deep Learning Technique

Efficient Monkeypox Detection in Skin Lesions Using Pre-Trai...

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2024 International conference on artificial Intelligence and Quantum Computation-Based Sensor applications, ICAIQSA 2024

作者： Vandana Sharma, Chetna Chahal, Ansh Daryal, Neeti Chitkara University Institute of Engineering and Technology Punjab Rajpura India Mukand Lal National College Kurukshetra University Yamuna nagar India

ISBN: (纸本)9798331517953

A medical emergency was declared by the World Health Organization and the Department of Health when the monkeypox virus gained prominence during the COVID-19 pandemic. Traditional classification systems struggle to accurately identify skin disorders because viruses that cause them often have similar skin appearances. Concerns about security and privacy are often raised when sharing sensitive medical data. A workable solution to the issues related to medical data categorization and illness detection is provided by the combination of Federated Learning and deep neural networks. The proposed technique recommends using pre-trained Deep-Learning technique in a federated learning technique to identify the virus that infects human skin. The Kaggle and open-source datasets were both used. In order to develop a global technique, the research sought to collaborate with various local datasets while maintaining privacy concerns. The proposed technique is simultaneously computing the categorical accuracy level, F1-score micro, and F1-score macro. The effectiveness of the proposed technique is validated by comparing the procured results with the existing state-of-art techniques. © 2024 IEEE.

关键词： Deep Learning Federated Learning image processing Monkeypox

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Padding-Free Convolution Based on Preservation of Differential Characteristics of Kernels 22

Padding-Free Convolution Based on Preservation of Differenti...

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22nd IEEE International conference on Machine Learning and applications, ICMLA 2023

作者： Leng, Kuangdai Thiyagalingam, Jeyan Science and Technology Facilities Council Scientific Computing Department Didcot United Kingdom

ISBN: (纸本)9798350345346

Convolution is a fundamental operation in image processing and machine learning. Aimed primarily at maintaining image size, padding is a key ingredient of convolution, which, however, can introduce undesirable boundary effects. We present a non-padding-based method for size-keeping convolution based on the preservation of differential characteristics of kernels. The main idea is to make convolution over an incomplete sliding window 'collapse' to a linear differential operator evaluated locally at its central pixel, which no longer requires information from the neighbouring missing pixels. While the underlying theory is rigorous, our final formula turns out to be simple: the convolution over an incomplete window is achieved by convolving its nearest complete window with a transformed kernel. This formula is computationally lightweight, involving neither interpolation or extrapolation nor restrictions on image and kernel sizes. Our method favours data with smooth boundaries, such as high-resolution images and fields from physics. Our experiments include: i) filtering analytical and non-analytical fields from computational physics and, ii) training convolutional neural networks (CNNs) for the tasks of image classification, semantic segmentation and super-resolution reconstruction. In all these experiments, our method has exhibited visible superiority over the compared ones. © 2023 IEEE.

关键词： computer vision convolutional neural network differential operator machine learning padding

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Inducing Inductive Bias in Vision Transformer for EEG Classification 49

Inducing Inductive Bias in Vision Transformer for EEG Classi...

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49th IEEE International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Khadka, Rabindra Lind, Pedro G. Mello, Gustavo Riegler, Michael A. Yazidi, Anis Simula Metropolitan Ctr Digital Engn Oslo Norway Nord Ctr Sustainable & Trustworthy Artificial Int Oslo Norway

ISBN: (纸本)9798350344868;9798350344851

Human brain signals are highly complex and dynamic in nature. Electroencephalogram (EEG) devices capture some of this complexity, both in space and in time, with a certain resolution. Recently, transformer-based models have been explored in various applications with different modalities of data. In this work, we introduce a transformer-based model for the classification of EEG signals, inspired by the recent success of the Vision Transformer (ViT) in image classification. Driven by the distinctive characteristics of the EEG data, we design a module that enables us to (1) extract spatio-temporal tokens inherent in the EEG signals and (2) integrate additional non-linearities to capture intricate and non-linear patterns in EEG signals. To that end, we introduce a new lightweight architectural component that combines our proposed attention model with convolution. This convolutional tokenization module forms the basis of our vision backbone referred to as Brain Signal Vision Transformer (BSVT). This architecture takes into account the spatial and temporal features in EEG datasets, leading to token embeddings that effectively capture the fusion of spatial-temporal information. Moreover, while transformer-based models typically perform well when provided with large datasets, here we show that our combination of the inherent inductive bias of Convolutional neural networks (CNN) with the transformer enables efficient training from scratch using relatively small datasets, with as few as 0.75M parameters. On the publicly available EEG dataset from Temple University Hospital (TUH Abnormal), our model achieves results comparable or superior to its counterpart ViT model with patchify stem. The implementation is available at https://***/IamRabin/***

关键词： EEG CNN ViT Tokenization Inductive bias

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5th EAI International conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

5th EAI International Conference on Cognitive Computing and ...

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5th EAI International conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

ISBN: (纸本)9783031770777

The proceedings contain 102 papers. The special focus in this conference is on Cognitive Computing and Cyber Physical Systems. The topics include: Comparative Analysis Between Fuzzy Theorem and KNN Methodology for Fault and Anomaly Detection;Production Classification in E-Commerce Based on Product Descriptions with Natural Language processing (NLP) and Machine Learning Models;defense Strategy Security Mechanism for Sensor networks;an Efficient Approach for Food Demand Forecasting Using an Ensemble Technique and Statistical Analysis;identification of Different Medicinal Plants Using Machine Learning and image processing;Leveraging the Power of MRMR in Machine Learning Models for Multi Class Classification of Rice to Promote Sustainable and Efficient Smart Farming;performance Analysis of Word Recognition System Using Tensor Flow;A Distinct artificial Feed Forward neural Network (AF2NN) Model for Predicting Compressive Strength of Geo-Polymer Concrete;Performance Assessment of Deep Learning-Models for Kidney Tumor Segmentation using CT images;investigation of Quantum Machine Learning for Smart Eco System Focusing on Energy Optimization;Classification of Skin Cancer Using CNN with Transformer Layer;a Unified Approach to Smart Coconut Farming with IoT and Deep Learning for Recommendation of Pesticides and Fertilizers;classifying the Severity of Diabetic Retinopathy Using Deep Learning;Exploring Prominent Convolutional neural Network Frameworks to Identify COVID-19 Deceases by Using Medical images;non-invasive Technique for Detecting Glycosuria Through image processing and Deep Learning Approaches;deep Learning Strategies for Multiclass Skin Disease Classification;AI-Driven Glaucoma Susceptibility Assessment and Lifestyle Guidance;Deep Learning in Cartoon Moderation: Distinguishing Child-Friendly Content with CNN Architectures;THz Wideband Metamaterial Absorber for Different applications.

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ALE-GAN: A 3D Conditional Generative Adversarial Network with Attention Learning Modules for Lung Nodule Segmentation 2nd

ALE-GAN: A 3D Conditional Generative Adversarial Network wit...

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2nd International conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Dabass, Manju Chandalia, Anuj Datta, Suvrankar Mahapatra, Dwarikanath AI Research and Development Department Manentia AI Advisory Private Limited. Gandhinagar India Radiodiagnosis and Interventional Radiology AlIMS New Delhi India R&D Department Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates

ISBN: (纸本)9789819995301

Early lung cancer detection using lung nodule segmentation can enhance patient survival. In computer vision applications like medical image analysis, convolutional neural networks (CNNs) outperform standard image processing methods. Convolutional neural network-based medical image segmentation methods have exhibited state-of-the-art performance, but they still face limitations. Data scarceness and class inequality induce overfitting and deprived performance. This study proposes a scalar attention learning modules (Sc-ALM) embedded generative adversarial network for lung nodule segmentation that learns nodule-focused data distribution to boost accuracy. The proposed network utilizes Sc-ALM in generator part based on U-Net as well as the discriminator based on a simple classification network that empowered the model to distinguish ground truth from falsified segmentation. Also, patch-based training reduces overfitting. The LUNA16 dataset is used to perform extensive lung nodule segmentation experiments. The proposed model’s boundary F1-score, average Jaccard index, and dice similarity coefficient are 0.9695, 0.9650, and 0.9825 respectively, outperforming various state-of-the-art models. This model has remarkable lung nodule segmentation performance and can help doctors diagnose lung nodules early by assessing their size, shape, and other properties. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Generative adversarial networks

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High-Accuracy and Energy-Efficient Action Recognition with Deep Spiking neural Network 29th

High-Accuracy and Energy-Efficient Action Recognition with D...

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29th International conference on neural Information processing

作者： Zhang, Jingren Wang, Jingjing Di, Xie Pu, Shiliang Hangzhou Hikvis Digital Technol Co Ltd Hikvis Res Inst Hangzhou Peoples R China

ISBN: (纸本)9783031301070;9783031301087

In recent years, spiking neural networks (SNNs) received significant attention as the third generation of networks and have successfully been employed in energy-efficient image classification tasks. However, typical SNN construction methods still suffer from problems such as high inference latency or incompatibility with complicated models. Thus, applications of SNNs are limited to relatively simple tasks. In this paper, we establish an SNN-based action recognition model which aims at a more challenging video classification task. Specifically, the action recognition SNN model with a deep two-stream architecture is constructed with a hybrid conversion method combining channel-wise normalization and tandem learning. A skipping-step rate decoder is applied to decrease the conversion errors and improve the transmission accuracy. A new conversion and inference method for recurrent spiking neural network (RSNN) is introduced into the framework. The tandem learning method with bounded ReLU (bReLU) function is employed to fine-tune the normalized SNN parameters, decreasing the inference latency while still preserving high accuracy. Experiments on UCF-101 show that our proposed model obtains an accuracy of 88.46% with only 200 time steps, which achieves a high-accuracy and energy-efficient performance in SNN.

关键词： spiking neural network action recognition ANN-to-SNN conversion tandem learning

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A Comparative Study about Content-Based image Retrieval using Features from Deep Convolutional neural networks 2

A Comparative Study about Content-Based Image Retrieval usin...

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2nd IEEE International conference on Communication, Security and artificial Intelligence, ICCSAI 2023

作者： Pinaki, Budi Srikrushna Reddy, Meka Dheeraj Kapardi, Srisatya M.V.S.R Engineering College Osmania University Department of Electronics and Communication Engineering Hyderabad India Vasavi College of Engineering Osmania University Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350369960

'Content-Based image Retrieval (CBIR)' is a popular approach that is frequently used to handle the image retrieval problem. Despite the fact that the CBIR approach has seen tremendous improvements, the fundamental challenge of finding and retrieving pertinent photos with high levels of accuracy remains. A lot of demand exists for the creation of efficient CBIR systems that utilise modern techniques. Convolutional neural networks (CNN) were used, which led to a substantial paradigm shift in machine learning and outstanding performance. This study's major objective is to ascertain whether deep CNN models can be employed to boost picture retrieval effectiveness. In a range of image-processing tasks, such as object identification, classification, and clustering, among others, CNNs excel very well. This work proposed the CBIR methodology, which implements the extraction of features by using a range of pre-trained CNN models. For this experimental research project, radiology and meteorological datasets were used. The findings demonstrated that pre-trained deep CNN models' learnt features may be quickly applied to image retrieval tasks and outperform hand-crafted features utilized in conventional CBIR techniques in terms of performance. A CBIR framework using features from a 'pre-trained deep CNN model (VGG16)' was provided in accordance with the study's findings. Put the recently created CBIR algorithm through its paces using a 'multi-class dataset' made up of images connected to the weather. The innovative CBIR technique resulted in an average retrieval precision of 86.73%. The standard CBIR design, which relies on manually created features (such color, texture, and form attribute), has produced an average retrieval precision of 73.25% in comparison to this. The unique methodology outperformed the traditional method, which showed a 13.48% larger gain in average precision. © 2023 IEEE.

关键词： Content-Based image Retrieval Convolutional neural networks Data Analysis Deep Learning image Extraction image Retrieval

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Application Analysis of Digital Special Effects Technology in Film and Television Post-production Based on neural Network Algorithm 4th

Application Analysis of Digital Special Effects Technology i...

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4th International conference on Machine Learning, image processing, Network Security and Data Sciences (MIND)

作者： Qian, Hongxing Changchun Humanities & Sci Coll Sch Media & Commun Changchun Jilin Peoples R China

ISBN: (纸本)9783031243660;9783031243677

The leap in film and television special effects technology has updated a series of film production methods. Post-production using the most advanced computer graphics technology stimulates the creativity of the producers, simplifies the post-production process, and improves the quality of the entire film. The purpose of this paper is to study the application of digital special effects technology in film and television post-production based on neural network algorithm. First, the digital technology used in the widely used film and television post-production is introduced, and then some applications and problems of artificial neural networks are introduced. Then the PointNet network structure is introduced, which is a deep learning network in 3D point cloud. Framework, which eliminates the ambiguity caused by the disorder and rotation of point clouds by introducing T-net and utilizing max-pooling, and finally we introduce an encoder-decoder network for 3D human reconstruction, which encodes The network extracts the features, and uses the decoding network to learn the transformation between the template and the input point cloud, so as to complete the deformation fitting between the template and the point cloud.

关键词： neural networks Film and television Post production Digital special effects technology

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