Diabetic Retinopathy Detection Using Machine Learning
Volume-4 | Issue-1
Monocular Depth Estimation using a Multi-grid Attention-based Model
Volume-4 | Issue-3
Speedy Image Crowd Counting by Light Weight Convolutional Neural Network
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Construction of Efficient Smart Voting Machine with Liveness Detection Module
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An Economical Robotic Arm for Playing Chess Using Visual Servoing
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Triplet loss for Chromosome Classification
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Unstructured Noise Removal for Industrial Sensor Imaging Unit by Hybrid Adaptive Median Algorithm
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Real Time Sign Language Recognition and Speech Generation
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Analysis of Artificial Intelligence based Image Classification Techniques
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Design of ANN Based Machine Learning Method for Crop Prediction
Volume-3 | Issue-3
A REVIEW ON IOT BASED MEDICAL IMAGING TECHNOLOGY FOR HEALTHCARE APPLICATIONS
Volume-1 | Issue-1
COMPUTER VISION BASED TRAFFIC SIGN SENSING FOR SMART TRANSPORT
Volume-1 | Issue-1
Diabetic Retinopathy Detection Using Machine Learning
Volume-4 | Issue-1
Accurate Segmentation for Low Resolution Satellite images by Discriminative Generative Adversarial Network for Identifying Agriculture Fields
Volume-3 | Issue-4
Deep Learning based Handwriting Recognition with Adversarial Feature Deformation and Regularization
Volume-3 | Issue-4
State of Art Survey on Plant Leaf Disease Detection
Volume-4 | Issue-2
Optimal Compression of Remote Sensing Images Using Deep Learning during Transmission of Data
Volume-3 | Issue-4
OverFeat Network Algorithm for Fabric Defect Detection in Textile Industry
Volume-3 | Issue-4
VIRTUAL RESTORATION OF DAMAGED ARCHEOLOGICAL ARTIFACTS OBTAINED FROM EXPEDITIONS USING 3D VISUALIZATION
Volume-1 | Issue-2
Two-Stage Frame Extraction in Video Analysis for Accurate Prediction of Object Tracking by Improved Deep Learning
Volume-3 | Issue-4
Volume - 4 | Issue - 2 | june 2022
Published
18 July, 2022
Image generation is the task of automatically generating an image using an input vector z. In recent years, the quest to understand and manipulate this input vector has gained more and more attention due to potential applications. The previous works have shown promising results in interpreting the latent space of pre-trained Generator G to generate images up to 256 x 256 using supervised and unsupervised techniques. This paper addresses the challenge of interpreting the latent space of pre-trained Generator G to generate high-resolution images, i.e., images with resolution up to 1024x1024. This problem is tackled by proposing a new framework that iterates upon Cyclic Reverse Generator (CRG) by upgrading Encoder E present in CRG to handle high-resolution images. This model can successfully interpret the latent space of the generator in complex generative models like Progressive Growling Generative Adversarial Network (PGGAN) and StyleGAN. The framework then maps input vector zf with image attributes defined in the dataset. Moreover, it gives precise control over the output of generator models. This control over generator output is tremendously helpful in enhancing computer vision applications like photo editing and face manipulation. One downside of this framework is the reliance on a comprehensive dataset, thus limiting the use of it.
KeywordsLatent Space Generative Adversarial Networks Encoders Computer Vision Supervised Learning
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