Transfer Learning
2819 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 with no code
Tao: Re-Thinking DL-based Microarchitecture Simulation
Microarchitecture simulators are indispensable tools for microarchitecture designers to validate, estimate, and optimize new hardware that meets specific design requirements.
High-Resolution Detection of Earth Structural Heterogeneities from Seismic Amplitudes using Convolutional Neural Networks with Attention layers
Earth structural heterogeneities have a remarkable role in the petroleum economy for both exploration and production projects.
Self-Supervised Learning Featuring Small-Scale Image Dataset for Treatable Retinal Diseases Classification
The proposed SSL model achieves the state-of-art accuracy of 98. 84% using only 4, 000 training images.
Multiple-Input Fourier Neural Operator (MIFNO) for source-dependent 3D elastodynamics
The MIFNO predictions are assessed as good to excellent based on Goodness-Of-Fit (GOF) criteria.
Intelligent Chemical Purification Technique Based on Machine Learning
We present an innovative of artificial intelligence with column chromatography, aiming to resolve inefficiencies and standardize data collection in chemical separation and purification domain.
FedDistill: Global Model Distillation for Local Model De-Biasing in Non-IID Federated Learning
This paper introduces FedDistill, a framework enhancing the knowledge transfer from the global model to local models, focusing on the issue of imbalanced class distribution.
Breast Cancer Image Classification Method Based on Deep Transfer Learning
To address the issues of limited samples, time-consuming feature design, and low accuracy in detection and classification of breast cancer pathological images, a breast cancer image classification model algorithm combining deep learning and transfer learning is proposed.
Low-Resource Named Entity Recognition with Cross-Lingual, Character-Level Neural Conditional Random Fields
Low-resource named entity recognition is still an open problem in NLP.
HEAT: Head-level Parameter Efficient Adaptation of Vision Transformers with Taylor-expansion Importance Scores
In this paper, we propose Head-level Efficient Adaptation with Taylor-expansion importance score (HEAT): a simple method that efficiently fine-tuning ViTs at head levels.
Advanced wood species identification based on multiple anatomical sections and using deep feature transfer and fusion
In recent years, we have seen many advancements in wood species identification.
