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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1701–1725 of 10307 papers

TitleStatusHype
Environment Sensing-aided Beam Prediction with Transfer Learning for Smart Factory—0
SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning—0
The Impact of Geometric Complexity on Neural Collapse in Transfer Learning—0
Transfer Learning with Informative Priors: Simple Baselines Better than Previously ReportedCode0
Disease-informed Adaptation of Vision-Language ModelsCode0
Detection and Positive Reconstruction of Cognitive Distortion sentences: Mandarin Dataset and Evaluation—0
CrossVoice: Crosslingual Prosody Preserving Cascade-S2ST using Transfer Learning—0
Multilingual Prosody Transfer: Comparing Supervised & Transfer Learning—0
An LSTM Feature Imitation Network for Hand Movement Recognition from sEMG SignalsCode0
Magnetic Resonance Image Processing Transformer for General Accelerated Image Reconstruction—0
Federated Domain-Specific Knowledge Transfer on Large Language Models Using Synthetic Data—0
Combining Denoising Autoencoders with Contrastive Learning to fine-tune Transformer ModelsCode0
Deep learning lattice gauge theories—0
Implicit In-context LearningCode1
SolNet: Open-source deep learning models for photovoltaic power forecasting across the globe—0
MAMOC: MRI Motion Correction via Masked Autoencoding—0
Fine-grained Image-to-LiDAR Contrastive Distillation with Visual Foundation ModelsCode1
Data-Free Federated Class Incremental Learning with Diffusion-Based Generative Memory—0
Multi-Dataset Multi-Task Learning for COVID-19 Prognosis—0
Boosted Neural Decoders: Achieving Extreme Reliability of LDPC Codes for 6G NetworksCode1
Dynamically enhanced static handwriting representation for Parkinson's disease detection—0
Transfer of Safety Controllers Through Learning Deep Inverse Dynamics Model—0
Traffic control using intelligent timing of traffic lights with reinforcement learning technique and real-time processing of surveillance camera images—0
Just rotate it! Uncertainty estimation in closed-source models via multiple queries—0
Near-Field Spot Beamfocusing: A Correlation-Aware Transfer Learning Approach—0
Show:102550
← PrevPage 69 of 413Next →
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