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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 576–600 of 10307 papers

TitleStatusHype
Forecasting Workload in Cloud Computing: Towards Uncertainty-Aware Predictions and Transfer LearningCode1
Semantic-Fused Multi-Granularity Cross-City Traffic PredictionCode1
ASSET: Robust Backdoor Data Detection Across a Multiplicity of Deep Learning ParadigmsCode1
Deep comparisons of Neural Networks from the EEGNet familyCode1
Towards Efficient Visual Adaption via Structural Re-parameterizationCode1
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image ClassificationCode1
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement LearningCode1
UniAdapter: Unified Parameter-Efficient Transfer Learning for Cross-modal ModelingCode1
SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural NetworksCode1
Mixed formulation of physics-informed neural networks for thermo-mechanically coupled systems and heterogeneous domainsCode1
Adapting Pre-trained Vision Transformers from 2D to 3D through Weight Inflation Improves Medical Image SegmentationCode1
DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size ScheduleCode1
Domain Adaptation for Time Series Under Feature and Label ShiftsCode1
Paced-Curriculum Distillation with Prediction and Label Uncertainty for Image SegmentationCode1
Knowledge Transfer from Pre-trained Language Models to Cif-based Speech Recognizers via Hierarchical DistillationCode1
A Closer Look at Few-shot Classification AgainCode1
Domain-Agnostic Molecular Generation with Chemical FeedbackCode1
MV-Adapter: Multimodal Video Transfer Learning for Video Text RetrievalCode1
Multimodal Side-Tuning for Document ClassificationCode1
TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning ProblemsCode1
MOTOR: A Time-To-Event Foundation Model For Structured Medical RecordsCode1
Language Models are Drummers: Drum Composition with Natural Language Pre-TrainingCode1
Novel Scenes & Classes: Towards Adaptive Open-set Object DetectionCode1
ERM-KTP: Knowledge-Level Machine Unlearning via Knowledge TransferCode1
ScaleFL: Resource-Adaptive Federated Learning With Heterogeneous ClientsCode1
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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