Few-Shot Learning Final Year Projects with Source Code

Byadmin Final Year Projects

Few-Shot Learning Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Few-Shot Learning 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.

Few-Shot Learning Final Year Projects

  1. Deep CleanerA Few Shot Image Dataset Cleaner Using Supervised Contrastive Learning
    This project focuses on improving the quality of medical images before they are used for AI diagnosis. It automatically removes noisy or unwanted parts of images using a learning model trained on only a few clean examples. The system learns to separate correct images from incorrect ones. After cleaning, the accuracy of disease classification improves significantly.
  2. Looking Closer to the Transferability Between Natural and Medical Images in Deep Learning
    This study looks at how to improve medical image analysis using machine learning. It tests if knowledge from natural images can help in medical imaging. The researchers found that using natural images does not improve results much because medical images are very different. They also studied data enhancement techniques and found that these methods don’t transfer well from natural to medical images.
  3. A Survey on Deep Learning for Website Fingerprinting Attacks and Defenses
    This project studies how people’s web browsing activity can be tracked and protected. It focuses on website fingerprinting, which identifies the websites a user visits. The research surveys how deep learning can be used both to perform these tracking attacks and to defend against them. It also reviews methods, challenges, and future directions in this area.
  4. An Enhanced Prototypical Network Architecture for Few-Shot Handwritten Urdu Character Recognition
    This project builds a system that can recognize handwritten Urdu characters using only a few sample images. It trains a model to learn patterns even when very little clean data is available. The system groups similar characters and uses these groups to identify new ones. It works better than many existing methods and gives higher accuracy with very limited training data.
  5. Enhancing Few-Shot Image Classification With Cosine Transformer
    This project focuses on teaching a computer to recognize images even when it has very few examples to learn from. The researchers developed a new method called Few-shot Cosine Transformer, which compares a small set of labeled images with new unlabeled images to improve accuracy. They use a special attention mechanism called Cosine Attention to make the model more reliable and efficient. This approach works well on standard datasets and can be applied in areas like healthcare, security, and pose recognition.
  6. Experimental Validation of Artificial Neural Network Based Road Condition Classifier and its Complementation
    This project focuses on using artificial intelligence to estimate how slippery a road is for each wheel of a car. The researchers improved an existing neural network by adding braking pressure as an input. They tested the system using real-world data and found it predicts road friction accurately in normal and challenging conditions. This method uses only sensors already in most cars, so no extra equipment is needed.
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Few-Shot Learning Project Synopsis & Presentation

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Few-Shot Learning Project Thesis Writing

Final Year Projects provides thesis writing services for Few-Shot Learning projects. We help BE, BTech, ME, MSc, MCA and MTech students complete their final year project work efficiently.

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