Implementation of the model-agnostic meta-learning framework on CWRU bearing fault dataset to address cross-domain few-shot fault diagnosis problem.
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Updated
Feb 14, 2025 - Python
Implementation of the model-agnostic meta-learning framework on CWRU bearing fault dataset to address cross-domain few-shot fault diagnosis problem.
Thesis work, University of Groningen : Lifelong 3D Object Recognition and Grasp Synthesis using Dual Memory Recurrent Self-Organization Networks
EN3150 - Pattern Recognition | University of Moratuwa A PyTorch-based Convolutional Neural Network (CNN) project for handwritten digit recognition using the MNIST dataset. Includes optimizer comparison (Adam, SGD, SGD+Momentum), momentum analysis, and transfer learning with pretrained ResNet18 and VGG16 models.
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