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 651–675 of 10307 papers

TitleStatusHype
Deep Data Augmentation for Weed Recognition Enhancement: A Diffusion Probabilistic Model and Transfer Learning Based ApproachCode1
Is synthetic data from generative models ready for image recognition?Code1
Unified Vision and Language Prompt LearningCode1
Token-Label Alignment for Vision TransformersCode1
Prompt Generation Networks for Input-Space Adaptation of Frozen Vision TransformersCode1
Task Compass: Scaling Multi-task Pre-training with Task PrefixCode1
Transfer Learning with Joint Fine-Tuning for Multimodal Sentiment AnalysisCode1
Association Graph Learning for Multi-Task Classification with Category ShiftsCode1
Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects EstimationCode1
Training Deep Learning Algorithms on Synthetic Forest Images for Tree DetectionCode1
Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-ExpertsCode1
Exploring Effective Knowledge Transfer for Few-shot Object DetectionCode1
Towards a Unified View on Visual Parameter-Efficient Transfer LearningCode1
Visual Prompt Tuning for Generative Transfer LearningCode1
Spectral Augmentation for Self-Supervised Learning on GraphsCode1
Hyper-Representations as Generative Models: Sampling Unseen Neural Network WeightsCode1
CALIP: Zero-Shot Enhancement of CLIP with Parameter-free AttentionCode1
Transfer Learning with Pretrained Remote Sensing TransformersCode1
An Empirical Study on Cross-X Transfer for Legal Judgment PredictionCode1
CoV-TI-Net: Transferred Initialization with Modified End Layer for COVID-19 DiagnosisCode1
Cross Project Software Vulnerability Detection via Domain Adaptation and Max-Margin PrincipleCode1
Toward Safe and Accelerated Deep Reinforcement Learning for Next-Generation Wireless NetworksCode1
ScreenQA: Large-Scale Question-Answer Pairs over Mobile App ScreenshotsCode1
Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer LearningCode1
KT-BT: A Framework for Knowledge Transfer Through Behavior Trees in Multi-Robot SystemsCode1
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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