codebro1847 - Peter Perez @codebro1847
Domain adaptation -Computer Vision- Pytorch-Django MY PUBLIC GITHUB - https://t.co/drL0JjqLXK YouTube - https://t.co/ZuHkhKk1kJ Chicago Joined February 2025-
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Partial Domain Adaptation (PDA) is a domain adaptation scenario where the target domain's label space is a subset of the source domain's label space #Domainadaption #pytorch #computervision github.com/measterpojo/Pa…
The Mean Teacher Model is where a student model learns from a more stable teacher model that updates through Exponential Moving Average (EMA). #Pytorch #deeplearning #Domainadaptation #PACS #meanteachermodel github.com/measterpojo/Me…
CNN-based MAML retains the standard principles of Model-Agnostic Meta-Learning (MAML) but integrates convolutional neural networks (CNNs) #MetaLearning #MachineLearning #MAML #CNN #MetaTraining github.com/measterpojo/Me…:
Hybrid CNN-ViT models combine the strengths of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to create a powerful architecture for image recognition #deeplearning #Python #ArtificialIntelligence #PyTorch #CNN #Transformers github.com/measterpojo/Hy…
Adversarial Discriminative Domain Adaptation (ADDA) is a DA approach that uses adversarial training to align the feature distributions of the source and target domains #DeepLearning #MachineLearning #DomainAdaptation #AdversarialAdaptation #pytorch github.com/measterpojo/Ad…
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…
This pipeline is a hybrid that combines prototype-based learning with domain adaptation and pseudo-labeling #PyTorch #Python #DeepLearning #AI #prototype #NeuralNetworks #DataScience github.com/measterpojo/Pr…
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…
Cross Pseudo Supervision (CPS) is a semi-supervised learning technique designed to improve semantic segmentation by leveraging unlabeled data more effectively #PyTorch #Pseudolabeling #DeepLearning #ai #Python github.com/measterpojo/Cr…
Contrastive learning is a powerful self-supervised technique for domain adaptation #Python #ai #contrastive #pytorch #domainadapatation github.com/measterpojo/Se…
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…
Object detection for Domain adaptation with The Pascal VOC 2012 dataset (DANN). After extensive hyperparameter tuning, I finally achieved good results. #PyTorch #deepleaning #ai #objectdetection #Python github.com/measterpojo/Ob…:
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