awesome-transfer-learning
github.com/artix41/awesome-transfer-learning ↗Best transfer learning and domain adaptation resources (papers, tutorials, datasets, etc.)
1.8k
GitHub Stars
146
Curated Resources
10
Categories
7 hours ago
Last Refreshed
SurveysDeep Transfer LearningUnsupervised Domain AdaptationSemi-supervised Domain AdaptationFew-shot Supervised Domain AdaptationApplied Domain AdaptationImage-to-imageText-to-textOtherDigits transfer (unsupervised)
Use this list with your AI agent
Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me adversarial methods resources from awesome-transfer-learning"
Installation instructions →What's inside
Resources
Unsupervised Domain Adaptation
- Adaptative Discriminative Domain AdaptationAdversarial methods
- Adapting Visual Category Models to New DomainsOther
- A DIRT-T Approach to Unsupervised Domain AdaptationAdversarial methods
- Algorithms and Theory for Multiple-Source AdaptationTheory
- A Simple Approach for Unsupervised Domain AdaptationKernel methods
- Associative Domain AdaptationEmbedding methods
Deep Transfer Learning
- A Deep Convolutional Neural Network for Location Recognition and Geometry Based InformationApplications
- CNN Features off-the-shelf: an Astounding Baseline for RecognitionFeature extraction (embedding) approach
- Comparison of deep transfer learning strategies for digital pathologyApplications
- Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?Applications
- Deep Convolutional Neural Networks forComputer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer LearningApplications
- Do Better ImageNet Models Transfer Better?Fine-tuning approach
Applied Domain Adaptation
- Adversarial Teacher-Student Learning for Unsupervised Domain AdaptationAudio Processing
- Autoencoder-based Unsupervised Domain Adaptation for Speech Emotion RecognitionAudio Processing
- Automated discovery of characteristic features of phase transitions in many-body localizationPhysics
- Identifying Quantum Phase Transitions with Adversarial Neural NetworksPhysics
- Learning to Pivot with Adversarial NetworksPhysics
- Semi-Supervised Monaural Singing Voice Separation With a Masking Network Trained on Synthetic MixturesAudio Processing
Surveys
Few-shot Supervised Domain Adaptation
- Augmented Cyclic Adversarial Learning for Domain AdaptationAdversarial methods
- Few-Shot Adversarial Domain AdaptationAdversarial methods
- Unified Deep Supervised Domain Adaptation and GeneralizationEmbedding methods
Showing a sample of 146 resources. View the full list on GitHub →