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 3751–3775 of 10307 papers

TitleStatusHype
Knowledge Distillation Label Smoothing: Fact or Fallacy?—0
Knowledge Transfer from Pre-trained Language Models to Cif-based Speech Recognizers via Hierarchical DistillationCode1
Global Flood Prediction: a Multimodal Machine Learning Approach—0
Unsupervised Domain Adaptation on Person Re-Identification via Dual-level Asymmetric Mutual Learning—0
Supervised and Contrastive Self-Supervised In-Domain Representation Learning for Dense Prediction Problems in Remote Sensing—0
Cross-Subject Deep Transfer Models for Evoked Potentials in Brain-Computer Interface—0
A Closer Look at Few-shot Classification AgainCode1
Restricted Orthogonal Gradient Projection for Continual Learning—0
Towards Precise Model-free Robotic Grasping with Sim-to-Real Transfer Learning—0
TransNet: Transferable Neural Networks for Partial Differential Equations—0
A Deep Learning Method for Comparing Bayesian Hierarchical ModelsCode0
Feasibility and Transferability of Transfer Learning: A Mathematical Framework—0
Probing Out-of-Distribution Robustness of Language Models with Parameter-Efficient Transfer Learning—0
Improving Text-based Early Prediction by Distillation from Privileged Time-Series Text—0
Transition-Aware Multi-Activity Knowledge TracingCode0
Domain-Agnostic Molecular Generation with Chemical FeedbackCode1
Transfer Learning in Deep Learning Models for Building Load Forecasting: Case of Limited DataCode0
A predictive physics-aware hybrid reduced order model for reacting flows—0
Heterogeneous Domain Adaptation for IoT Intrusion Detection: A Geometric Graph Alignment Approach—0
Transfer Learning for Olfactory Object Detection—0
Truveta Mapper: A Zero-shot Ontology Alignment FrameworkCode0
Learned Interferometric Imaging for the SPIDER InstrumentCode0
Cross-lingual German Biomedical Information Extraction: from Zero-shot to Human-in-the-Loop—0
Classification of Luminal Subtypes in Full Mammogram Images Using Transfer Learning—0
Efficient Language Model Training through Cross-Lingual and Progressive Transfer LearningCode0
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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