Variational Autoencoder Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Variational Autoencoder projects give practical experience and help complete final-year submissions. All projects follow IEEE standards and each project includes source code, project thesis report, presentation, project execution and explanation.

Variational Autoencoder Final Year Projects

Constructing a Meta-Learner for Unsupervised Anomaly Detection

This project focuses on automatically picking the best anomaly detection method for any given dataset without needing labeled data. The researchers created a meta-learning system that looks at characteristics of the dataset to suggest the most suitable algorithm. They tested it on over 10,000 datasets and found it works better than existing methods. The study also shows that while a few dataset features are enough, the choice of the meta-learning model strongly affects performance.
Classification of Diabetic Retinopathy Disease Levels by Extracting Topological Features Using Graph Neural Networks

This project focuses on improving the detection of diabetic retinopathy, a major cause of blindness, from retinal images. It uses a new deep learning approach that combines feature extraction and graph-based analysis to better capture important details in the images. The model was tested on public datasets and showed higher accuracy and reliability than existing methods. It helps doctors by making disease diagnosis faster and more precise.
A Systematic Review of Facial Expression Detection Methods

This project studies how computers can recognize human emotions from facial expressions. It reviews many research studies that use deep learning techniques, especially convolutional neural networks. The work compares different methods and datasets to see which are most accurate. It helps understand which AI models work best for emotion detection.
Deep Generative Knowledge Distillation by Likelihood Finetuning

This project trains a small model using a larger model without needing real data. Instead, it creates artificial images using a special generator network. The method learns what the teacher model knows and produces samples that help the student model learn well. It aims to match the accuracy of top methods while using fewer generated samples and less time.
Multimodal Deep Learning Model of Predicting Future Visual Field for Glaucoma Patients

This project aims to predict how glaucoma will affect a patient’s vision in the future. It uses a deep learning model that looks at past vision test results and eye scan images to make predictions. The system combines image analysis with previous test data to improve accuracy. It also identifies and handles noisy or unreliable data, making the predictions more reliable for monitoring glaucoma progression.

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Variational Autoencoder Final Year Projects with Source Code