What question did this study set out to answer?

To evaluate the effectiveness of a deep learning model for image generation and its practical applications.

April 7, 20260 citationsOpen Access

A Study on Image Generation Using Deep Learning

Key Points

To evaluate the effectiveness of a deep learning model for image generation and its practical applications.
Developed a lightweight image generation system using Python on Google Colab.
Utilized a convolutional neural network (CNN) for feature extraction and image reconstruction.
Applied preprocessing techniques including image resizing and normalization to enhance the model's learning efficiency.
Optimized the model using the Adam optimizer and Mean Squared Error (MSE) loss function.
Achieved stable convergence with minimal overfitting during training.
Generated images maintained important structural and visual features.
Demonstrated that deep learning models can perform well in image generation tasks even in resource-limited scenarios.

Abstract

A Study on Image Generation Using Deep Learning Kajal Ananda Gavali PG Student, School of Business, Chhatrapati Shahu Institute of Business Education and Research, Kolhapur (MS), India Dr. Sachin Jagtap Assistant Professor, School of Business, Chhatrapati Shahu Institute of Business Education and Research, Kolhapur (MS), India Abstract Deep learning–based image generation has gained prominence in computer vision due to its applications in automation and artificial intelligence. This study examines the design and implementation of a lightweight and interpretable image generation system with reference to Softron Development, Kolhapur. The model is developed using Python on the Google Colab platform, incorporating preprocessing techniques such as image resizing, normalization, and dataset splitting to improve learning efficiency. A convolutional neural network (CNN) architecture is employed for feature extraction and image reconstruction, optimized using the Adam optimizer and Mean Squared Error (MSE) loss function. Experimental results indicate stable convergence with minimal overfitting, while generated images retain key structural and visual features. The findings demonstrate that computationally efficient deep learning models can deliver satisfactory performance in image generation tasks, particularly in resource-constrained environments. The study offers practical implications for academic training, industrial applications, and rapid prototyping. Keywords: Deep Learning, Image Generation, Convolutional Neural Network (CNN), Image Reconstruction, Machine Learning, Computer Vision, Python Implementation

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