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…
github.com
GitHub - measterpojo/PartialDomainAdpatation: Partial Domain Adaptation (PDA) is a domain adaptat...
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 - measterpojo/PartialDomainAdpatation
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…:
github.com
GitHub - measterpojo/Meta-Learning-MAML-PACS-Dataset: A CNN-based MAML retains the standard...
A CNN-based MAML retains the standard principles of Model-Agnostic Meta-Learning (MAML) but integrates convolutional neural networks (CNNs) as the base architecture for feature extraction and adapt...
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…
github.com
GitHub - measterpojo/Residual-Transfer-Network-RTN-for-Unsupervised-DA: The Residual Transfer...
The Residual Transfer Network (RTN) is a framework designed for domain adaptation - measterpojo/Residual-Transfer-Network-RTN-for-Unsupervised-DA
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…
github.com
GitHub - measterpojo/Contrastive-Semantic-Alignment-CCSA-Loss-Supervised-DA: CCSA Loss works by...
CCSA Loss works by minimizing the distance between samples of the same class from different domains while maximizing the distance between samples of different classes. - measterpojo/Contrastive-Sem...
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…
github.com
GitHub - measterpojo/Cross-Pseudo-Supervision-CPS-semi-supervised-segmentation: Cross Pseudo...
Cross Pseudo Supervision (CPS) is a semi-supervised learning technique designed to improve semantic segmentation by leveraging unlabeled data more effectively. - measterpojo/Cross-Pseudo-Supervisio...
Contrastive learning is a powerful self-supervised technique for domain adaptation #Python #ai #contrastive #pytorch #domainadapatation github.com/measterpojo/Se…
github.com
GitHub - measterpojo/Self-supervised-learning-SSL-Contrastive-Domain-Adaptation: Contrastive...
Contrastive learning is a powerful self-supervised technique for domain adaptation (DA) in PyTorch. It trains models to bring similar samples (positive pairs) closer and push different samples (neg...
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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