Transfer Learning
2856 papers with code • 7 benchmarks • 15 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
Asking and Answering Questions to Extract Event-Argument Structures
Transformer-based questions are generated using large language models trained to formulate questions based on a passage and the expected answer.
On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case
western-oc2-lab/tinyml_evci
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As technology advances, the use of Machine Learning (ML) in cybersecurity is becoming increasingly crucial to tackle the growing complexity of cyber threats.
Where to Mask: Structure-Guided Masking for Graph Masked Autoencoders
To this end, we introduce a novel structure-guided masking strategy (i. e., StructMAE), designed to refine the existing GMAE models.
Employing Two-Dimensional Word Embedding for Difficult Tabular Data Stream Classification
Rapid technological advances are inherently linked to the increased amount of data, a substantial portion of which can be interpreted as data stream, capable of exhibiting the phenomenon of concept drift and having a high imbalance ratio.
Unified Unsupervised Salient Object Detection via Knowledge Transfer
moothes/a2s-v2
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Firstly, we propose a Progressive Curriculum Learning-based Saliency Distilling (PCL-SD) mechanism to extract saliency cues from a pre-trained deep network.
Automated Long Answer Grading with RiceChem Dataset
luffycodes/automated-long-answer-grading
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With this work, we offer a fresh perspective on grading long, fact-based answers and introduce a new dataset to stimulate further research in this important area.
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.
Evaluating Fast Adaptability of Neural Networks for Brain-Computer Interface
anp-scp/fast_bci
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Nevertheless, there is a need for an evaluation strategy to evaluate the fast adaptability of the models, as this characteristic is essential for real-life BCI applications for quick calibration.
