SOTAVerified

Transfer Learning

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 )

Papers

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Showing 3451–3475 of 10307 papers

TitleStatusHype
Learning to search for and detect objects in foveal images using deep learning—0
Edge-cloud Collaborative Learning with Federated and Centralized Features—0
Model Sparsity Can Simplify Machine UnlearningCode1
Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning—0
Conditional Adapters: Parameter-efficient Transfer Learning with Fast Inference—0
A Self-attention Knowledge Domain Adaptation Network for Commercial Lithium-ion Batteries State-of-health Estimation under Shallow Cycles—0
Reinforcement Learning Based Minimum State-flipped Control for the Reachability of Boolean Control Networks—0
Feudal Graph Reinforcement LearningCode0
A Deep Analysis of Transfer Learning Based Breast Cancer Detection Using Histopathology Images—0
The MONET dataset: Multimodal drone thermal dataset recorded in rural scenariosCode1
Automaton-Guided Curriculum Generation for Reinforcement Learning AgentsCode0
Transfer Learning Across Heterogeneous Features For Efficient Tensor Program GenerationCode0
An Efficient Transfer Learning-based Approach for Apple Leaf Disease Classification—0
Simulated Annealing in Early Layers Leads to Better GeneralizationCode0
Recent Advancements in Machine Learning For Cybercrime Prediction—0
iPINNs: Incremental learning for Physics-informed neural networks—0
ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning—0
Transfer Learning for Low-Resource Sentiment AnalysisCode0
Detection of COVID19 in Chest X-Ray Images Using Transfer Learning—0
SimbaML: Connecting Mechanistic Models and Machine Learning with Augmented DataCode0
Domain Adaptation for Inertial Measurement Unit-based Human Activity Recognition: A Survey—0
Graph Enabled Cross-Domain Knowledge Transfer—0
Local Rose Breeds Detection System Using Transfer Learning Techniques—0
DATE: Domain Adaptive Product Seeker for E-commerceCode0
Privacy-Preserving CNN Training with Transfer Learning: Multiclass Logistic RegressionCode0
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All datasetsOffice-Home100 sleep nights of 8 caregiversBanglaLekha Isolated DatasetCOCO70KITTI Object Tracking Evaluation 2012Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2—Unverified
2DFA-ENTAccuracy69.2—Unverified
3DFA-SAFNAccuracy69.1—Unverified
4EasyTLAccuracy63.3—Unverified
5MEDAAccuracy60.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23—Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74—Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95—Unverified