Transfer Learning
2824 papers with code • 7 benchmarks • 14 datasets
Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.
( Image credit: Subodh Malgonde )
Benchmarks
These leaderboards are used to track progress in Transfer Learning
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Libraries
Use these libraries to find Transfer Learning models and implementationsDatasets
KITTI
Office-Home
Sims4Action
MFRC
CARLANE Benchmark
BanglaLekha-Isolated
SpaRTUN
fluocells
Retinal Fundus MultiDisease Image Dataset (RFMiD)
Subtasks
Latest papers
CloudS2Mask: A novel deep learning approach for improved cloud and cloud shadow masking in Sentinel-2 imagery
DPIRD-DMA/CloudS2Mask
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Remote Sensing of Environment 2024
Precise and efficient cloud and cloud shadow masking methods are required for the automated use of this data.
Unified Unsupervised Salient Object Detection via Knowledge Transfer
Firstly, we propose a Progressive Curriculum Learning-based Saliency Distilling (PCL-SD) mechanism to extract saliency cues from a pre-trained deep network.
ArtNeRF: A Stylized Neural Field for 3D-Aware Cartoonized Face Synthesis
In this framework, we utilize an expressive generator to synthesize stylized faces and a triple-branch discriminator module to improve the visual quality and style consistency of the generated faces.
CREST: Cross-modal Resonance through Evidential Deep Learning for Enhanced Zero-Shot Learning
Zero-shot learning (ZSL) enables the recognition of novel classes by leveraging semantic knowledge transfer from known to unknown categories.
Conditional Prototype Rectification Prompt Learning
chenhaoxing/cpr
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Recent advancements in efficient transfer learning (ETL) have shown remarkable success in fine-tuning VLMs within the scenario of limited data, introducing only a few parameters to harness task-specific insights from VLMs.
Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentation
mqinghe/midss
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To fully utilize the information within the intermediate domain, we propose a symmetric Guidance training strategy (SymGD), which additionally offers direct guidance to unlabeled data by merging pseudo labels from intermediate samples.
Using Explainable AI and Transfer Learning to understand and predict the maintenance of Atlantic blocking with limited observational data
This work demonstrates the potential for machine learning methods to extract meaningful precursors of extreme weather events and achieve better prediction using limited observational data.
Investigating Neural Machine Translation for Low-Resource Languages: Using Bavarian as a Case Study
Machine Translation has made impressive progress in recent years offering close to human-level performance on many languages, but studies have primarily focused on high-resource languages with broad online presence and resources.
E3: Ensemble of Expert Embedders for Adapting Synthetic Image Detectors to New Generators Using Limited Data
arefaz/e3-ensemble-of-expert-embedders-cvprwmf24
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To address these issues, we introduce the Ensemble of Expert Embedders (E3), a novel continual learning framework for updating synthetic image detectors.
OpenTrench3D: A Photogrammetric 3D Point Cloud Dataset for Semantic Segmentation of Underground Utilities
We present OpenTrench3D, a novel and comprehensive 3D Semantic Segmentation point cloud dataset, designed to advance research and development in underground utility surveying and mapping.
