codebro1847's profile picture. Domain adaptation -Computer Vision- Pytorch-Django

MY PUBLIC GITHUB - https://github.com/measterpojo

YouTube - https://www.youtube.com/@codebro1847

codebro1847 - Peter Perez

@codebro1847

Domain adaptation -Computer Vision- Pytorch-Django MY PUBLIC GITHUB - https://github.com/measterpojo YouTube - https://www.youtube.com/@codebro1847

Hierarchical Bayesian models are parameter-based because they define a structured probabilistic framework where parameters are learned at different levels of abstraction #Python #PyTorch #AI #DeepLearning #MachineLearning github.com/measterpojo/Hi…


The Residual Transfer Network (RTN) is a robust framework tailored for unsupervised domain adaptation tasks. It stands out by leveraging both deep residual learning and domain adaptation techniques github.com/measterpojo/Re…: #MachineLearning #DomainAdaptation


Minimax is an adversarial optimization framework where the encoder maximizes uncertainty to align features across domains, while the classifier minimizes uncertainty to maintain robust decision boundaries. #PyTorch #ai #DomainAdaptation #MachineLearning github.com/Minimax-Entrop…


Cosine similarity-based classifier instead of relying on traditional distance metrics like Euclidean distance, it measures the angle between vectors in the feature space to determine similarity. #PyTorch #DeepLearning #MachineLearning #ai #python github.com/measterpojo/co…


CCSA Loss is particularly well-suited for supervised Domain adaptation. Minimizing the distance between samples of the same class from different domains and maximizing the distance between samples of different classes. #PyTorch #python #AI #DeepLearning github.com/measterpojo/Co…


Using CycleGAN to address domain shift and fine-tuning a classifier on the adapted dataset is effective method for bridging gaps between source and target domains #ai #PyTorch #Python #DeepLearning #cycleGAN github.com/measterpojo/Cy…


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