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WHEN, WHY, AND WHICH PRETRAINED GANS ARE USEFUL? 10

WHEN, WHY, AND WHICH PRETRAINED GANS ARE USEFUL?

引用

10th International conference on Learning Representations, ICLR 2022

作者： Grigoryev, Timofey voynov, Andrey Babenko, Artem Yandex Russia

The literature has proposed several methods to finetune pretrained GANs on new datasets, which typically results in higher performance compared to training from scratch, especially in the limited-data regime. However, despite the apparent empirical benefits of GAN pretraining, its inner mechanisms were not analyzed in-depth, and understanding of its role is not entirely clear. Moreover, the essential practical details, e.g., selecting a proper pretrained GAN checkpoint, currently do not have rigorous grounding and are typically determined by trial and error. This work aims to dissect the process of GAN finetuning. First, we show that initializing the GAN training process by a pretrained checkpoint primarily affects the model's coverage rather than the fidelity of individual samples. Second, we explicitly describe how pretrained generators and discriminators contribute to the finetuning process and explain the previous evidence on the importance of pretraining both of them. Finally, as an immediate practical benefit of our analysis, we describe a simple recipe to choose an appropriate GAN checkpoint that is the most suitable for finetuning to a particular target task. Importantly, for most of the target tasks, Imagenet-pretrained GAN, despite having poor visual quality, appears to be an excellent starting point for finetuning, resembling the typical pretraining scenario of discriminative computer vision models. © 2022 ICLR 2022 - 10th International conference on Learning Representationss. All rights reserved.

关键词： III-v semiconductors

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Fire Detection and Autonomous Evacuation Guidance System

Fire Detection and Autonomous Evacuation Guidance System

引用

Intelligent data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： Sandyarani vadlamudi D Roja Ramani S Preetham Ganesh Dagadi Nipun Goyal Harshavardhan v Mantri Department of AIML New Horizon College of Engineering Bengaluru India Department of CSE New Horizon College of Engineering Bengaluru India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Effective fire emergency responses are critical to ensuring safety and minimizing damage. This research exhibits an innovative fire detection and evacuation system that utilizes deep-learning and real-time video analysis to improve response times and efficiency. Utilizing convolution neural nets (CNNs), the system is able to detect fires with high precision and minimal delay, while dynamically generating evacuation plans depending on the fire's location. The system integrates advanced machine learning techniques to analyse building layouts and determine the safest, shortest evacuation routes. This information is conveyed to occupants via a public address system, enabling a streamlined and well-coordinated evacuation process. By combining real-time fire detection with intelligent route optimization, the system offers a significant advancement over traditional fire safety technologies. The paper also addresses key challenges such as processing latency and the system's ability to operate effectively in real-world conditions. Experimental results demonstrate the reliability of the CNN model—Inception v3 for fire recognition and Dijkstra algorithm for route planning—in both accuracy and speed aspects. The system's ability to handle complex visual data while maintaining operational efficiency is a highlight of this research. Along with technical details, the research explores the practical implications of implementing this system in various environments. Key challenges include maintaining low latency in video streaming and fire detection, as well as ensuring the effectiveness of the escape route analysis in complex environments. Future improvements focus on scalability, with plans to expand the system's capabilities to support multi-camera setups and larger buildings, making it adaptable for widespread use.

关键词： visualization Scalability Buildings Neural networks Streaming media Real-time systems Convolutional neural networks Time factors Reliability Optimization

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Advancing Sleep Studies: An exploration of Phase-Based Approaches for Sleep Staging

Advancing Sleep Studies: An Exploration of Phase-Based Appro...

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Irish Signals and Systems conference (ISSC)

作者： Christopher McCausland Pardis Biglarbeigi Raymond Bond Dewar Finlay Nanotechnology and Integrated BioEngineering Centre Ulster University Belfast U.K. School of Science and Engineering University of Dundee Dundee U.K. School of Computing Ulster University Belfast U.K.

ISBN: (数字)9798350352986

ISBN: (纸本)9798350352993

Sleep staging is an important but burdensome process required for the assessment of overnight polysomnography. A major criticism of sleep staging is the unacceptable levels of inter-scorer reliability, caused in part, by the subjectiveness of visual interpretation. More recent literature has also proposed that differing regions of the brain can be in different sleep stages simultaneously which may add further confusion. This study assessed the potential of phase-based methods to discern sleep stages. Phase-based features were generated from inter-electrode and sub-frequency-bands of EEG signals. Classification was subsequently undertaken with a support vector machine used due to the high dimenionality of the data. The SvM was trained on 213,055 sleep stage observations with 23,675 test observations. An accuracy of 80.4% was achieved which was inline with current interscorer reliability maximums, without over-fitting. Observed performance metrics were 0.80, 0.80, 0.95, 0.80 and 0.75 for Sensitivity, PPv, Specificity, F1 Score and Cohen's kappa respectively.

关键词： Support vector machines Band-pass filters Training Time-frequency analysis visualization Correlation Accuracy

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Neural Network Algorithm of video analysis for Backup Local Navigation System of Tethered UAvs with Training on Synthetic data

Neural Network Algorithm of Video Analysis for Backup Local ...

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Information, Control, and Communication Technologies (ICCT), International conference on

作者： Evgeniy A. Lesiv Konstantin A. vytovtov Elizaveta A. Barabanova Russian Academy of Sciences V. A. Trapeznikov Institute of Control Sciences Moscow Russia

This paper discusses the problem of stabilizing a multicopter over a tethered high-altitude platform by using visual analysis systems to determine the position and orientation of the target platform. We propose a neural network architecture to solve the problem of backup local navigation system of tethered unmanned aerial vehicles in GPS-Denied and GLONASS-Denied environments. The input of the system is a video frame from the multicopter's onboard camera, and the output is the estimates of displacement and rotation of the tethered high-altitude platform. This research proposes a technique for synthesizing training examples based on the application of 3D graphics tools. Thus, the neural network is trained on synthetic data generated in a virtual environment.

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Web Based Real Time Bowel Cancer Stage Suggestion Using CNN

Web Based Real Time Bowel Cancer Stage Suggestion Using CNN

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Sustainable Computing and Smart Systems (ICSCSS), International conference on

作者： v Kandasamy. M Aravind. P Siva Ganesh. G Thulasi Raman. Department Of Information Technology Panimalar Engineering College Chennai India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Cancer that develops in the colon or rectum of the large intestine is called bowel cancer (CRC), although it goes by other names as well. The early detection of bowel cancer relies largely on the microscopic inspection of entire tissues samples. Due to its inherent subjectivity, the conventional visual examination is both laborious and wildly inaccurate. Using a deep learning method based on Convolutional Neural Networks (CNN) to distinguish between tumor tissue and healthy tissues, we provide a web-based flask application that may predict the stage of bowel cancer. 4550 image data of seven different tissue types in total are collected and analyzed using pandas and sklearn. A CNN model is trained after preprocessing and cleaning of data, the accuracy of the model is calculated and analyzed using Confusion Matrix. The model is deployed to a production environment using Flask as a Web Application. Though the information may not replace the advice of a doctor, the Application may serve as an intermediate between traditional and automated analysis of tissue for fast prediction of the stage of the cancer by Doctors.

关键词： Deep learning Analytical models visualization Accuracy Medical services Real-time systems data models

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An Inverse Modelling Framework for SAR Imagery analysis Built on SARCASTIC v2.0

An Inverse Modelling Framework for SAR Imagery Analysis Buil...

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IEEE National conference on Radar

作者： Michael Woollard Hugh Griffiths Matthew Ritchie Electronic and Electrical Engineering Department University College London London

ISBN: (数字)9798350329209

ISBN: (纸本)9798350329216

The analysis and interpretation of SAR imagery is widely recognised to be a challenging problem. The number and nature of non-intuitive effects often complicate human visual analysis, whilst the wide variation of target scattering behaviour over extended operating conditions is well-known to hinder automated processing. In this paper, we demonstrate how the SARCASTIC simulation engine is being extended to support answering analytical questions which would typically require significant input from expert analysts through inverse modelling approaches. This is a critical step towards enabling the exploitation of SAR collections at scale, which will become increasingly important as New Space continues to increase the number of taskable sensors on orbit. A case study examining an unusual urban target is presented, utilising real SAR collection data from an Umbra SAR satellite and matching simulations produced using the SARCASTIC toolset. An example analytical approach is demonstrated, and several opportunities for model-aided exploitation are identified. Preliminary results demonstrate promising correlation between the real and synthetic datasets.

关键词： Space vehicles Analytical models visualization Inverse problems Spaceborne radar Scattering Radar polarimetry

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PLEADES: Population Level Observation of Smartphone Sensed Symptoms for In-the-wild data using Clustering 16

PLEADES: Population Level Observation of Smartphone Sensed S...

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16th International Joint conference on Computer vision, Imaging and Computer Graphics Theory and Applications (vISIGRAPP) / 12th International conference on Information visualization Theory and Applications (IvAPP)

作者： Mansoor, Hamid Gerych, Walter Alajaji, Abdulaziz Buquicchio, Luke Chandrasekaran, Kavin Agu, Emmanuel Rundensteiner, Elke Worcester Polytech Inst Dept Data Sci Worcester MA 01609 USA Worcester Polytech Inst Dept Comp Sci Worcester MA 01609 USA

ISBN: (纸本)9789897584886

Smartphones are increasingly being used for health monitoring. Training of machine learning health models require studies in which smartphone sensor data is gathered passively on subjects' phones. Subjects live their lives 'In-the-wild" and periodically annotate data with ground truth health labels. While computational approaches such as machine learning produce accurate results, they lack explanations about the complex factors behind the manifestation of health-related symptoms. Additionally, population-level insights are desirable for scalability. We propose Population Level exploration and analysis of smartphone DEtected Symptoms (PLEADES), a framework to present smartphone sensed data in linked panes using intuitive data visualizations. PLEADES utilizes clustering and dimension reduction techniques for discovery of groupings of similar days based on smartphone sensor values, across users for population level analyses. PLEADES allows analysts to apply different clustering and projection algorithms to a given dataset and then overlays human-provided contextual and symptom information gathered during data collection studies, which empower the analyst in interpreting findings. Such overlays enable analysts to contextualize the symptoms that manifest in smartphone sensor data. We visualize two real world smartphone-sensed datasets using PLEADES and validate it in an evaluation study with data visualization and human context recognition experts.

关键词： Interactive visual Analytics Smartphone Sensed Health In-the-wild Smartphone data

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23rd International conference on Engineering Applications of Neural Networks, EANN 2022

23rd International Conference on Engineering Applications of...

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23rd International conference on Engineering Applications of Neural Networks, EANN 2022

ISBN: (纸本)9783031082221

The proceedings contain 42 papers. The special focus in this conference is on Engineering Applications of Neural Networks. The topics include: Learning Image Captioning as a Structured Transduction Task;Reconstructing Electricity Profiles in Submetering Systems Using a GRU-AE Network;trend and Seasonality Elimination from Relational data;Using a Deep Neural Network in a Relative Risk Model to Estimate vaccination Protection for COvID-19;A Gene Ontology-Driven Wide and Deep Learning Architecture for Cell-Type Classification from Single-Cell RNA-seq data;Brain Tumour Segmentation on 3D MRI Using Attention v-Net;route Scheduling System for Multiple Self-driving Cars Using K-means and Bio-inspired Algorithms;Semantic Segmentation of Diabetic Retinopathy Lesions, Using a UNET with Pretrained Encoder;efficient Approaches for data Augmentation by Using Generative Adversarial Networks;a Robust, Quantization-Aware Training Method for Photonic Neural Networks;towards Using Reinforcement Learning for Autonomous Docking of Unmanned Surface vehicles;an exploration of Semi-supervised Text Classification;SNNs Model Analyzing and visualizing Experimentation Using RAvSim;A Custom State LSTM Cell for Text Classification Tasks;Text analysis of COvID-19 Tweets;Automatic Accent and Gender Recognition of Regional UK Speakers;novel Decision Forest Building Techniques by Utilising Correlation Coefficient Methods;on Forecasting Project Activity Durations with Neural Networks;human Activity Recognition Under Partial Occlusion;preface;an Accurate Convolutional Neural Networks Approach to Wound Detection for Farmed Salmon;ITSC Fault Diagnosis in Permanent Magnet Synchronous Motor Drives Using Shallow CNNs;synopsis of video Files Using Neural Networks;trojanDroid: Android Malware Detection for Trojan Discovery Using Convolutional Neural Networks;generation of Orthogonality for Feature Spaces in the Bio-inspired Neural Networks;echo State Networks in data Marketplaces for Digital Content Creatio

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Multi-modal Masked Autoencoders for Medical vision-and-Language Pre-training 25th

Multi-modal Masked Autoencoders for Medical Vision-and-Langu...

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25th International conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)

作者： Chen, Zhihong Du, Yuhao Hu, Jinpeng Liu, Yang Li, Guanbin Wan, Xiang Chang, Tsung-Hui Chinese Univ Hong Kong Shenzhen Res Inst Big Data Shenzhen Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Pazhou Lab Guangzhou Peoples R China

ISBN: (纸本)9783031164439;9783031164422

Medical vision-and-language pre-training provides a feasible solution to extract effective vision-and-language representations from medical images and texts. However, few studies have been dedicated to this field to facilitate medical vision-and-language understanding. In this paper, we propose a self-supervised learning paradigm with multi-modal masked autoencoders (M(3)AE), which learn cross-modal domain knowledge by reconstructing missing pixels and tokens from randomly masked images and texts. There are three key designs to make this simple approach work. First, considering the different information densities of vision and language, we adopt different masking ratios for the input image and text, where a considerably larger masking ratio is used for images. Second, we use visual and textual features from different layers to perform the reconstruction to deal with different levels of abstraction in visual and language. Third, we develop different designs for vision and language decoders (i.e., a Transformer for vision and a multi-layer perceptron for language). To perform a comprehensive evaluation and facilitate further research, we construct a medical vision-and-language benchmark including three tasks. Experimental results demonstrate the effectiveness of our approach, where state-of-the-art results are achieved on all downstream tasks. Besides, we conduct further analysis to better verify the effectiveness of different components of our approach and various settings of pre-training. The source code is available at https://***/zhjohnchan/M3AE.

关键词： Multi-modal pre-training Masked autoencoders Medical vision-and-language analysis

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Predictive analysis of Consumer Behavior Using Supervised Learning Techniques

Predictive Analysis of Consumer Behavior Using Supervised Le...

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International conference on Computational and Characterization Techniques in Engineering & Sciences (CCTES)

作者： K. Sashi Rekha T. Amutha R. Usharani S. Pushparani v.M. Sivagami Department of Computer Science and Engineering SIMATS Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Artificial Intelligence & Data Science CARE College of Engineering Trichy Tamil Nadu India Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Meenakshi College of Engineering Chennai Tamil Nadu India Department of Information Technology Sri Venkateswara College of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

The study presented here consists of an entire framework for prediction of consumers behavior through the machine training of supervised learning methods. The strategy involves the following: data collection, preprocessing, exploration, feature selection, model selection, training, evaluation, interpretation and deployment. The systematic process is tailored to clear the pathway for combining data about customer taste, sophisticated habits, and purchasing trends. Primary components include data preprocessing, training of machine learning models, feature selection, and ethical issues regarding data privacy and transparency. The outcomes of the implementation are a high accuracy in consumers clustering, efficient classification of customers' behavior, as well as unveiling a bunch of crucial predictors that affect the customers mood and behavior. As a result, the report stresses the necessity of integrated monitoring and redirection strategies to realize the potential of the created models. The idea of predictive analysis as an evolving tool that enables marketers to tailor their campaigns more closely, and improve the overall customer experience therefore also receives attention. The proposed system methodology constitutes a solid process framework for business defined to aim at using predictive analytics to gain competitive edge in the current dynamic marketplace.

关键词： Training Adaptation models Consumer behavior Supervised learning data preprocessing Reinforcement learning Feature extraction data models Predictive analytics Stress

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