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 1301–1325 of 10307 papers

TitleStatusHype
Modularized data-driven approximation of the Koopman operator and generator—0
Interactive DualChecker for Mitigating Hallucinations in Distilling Large Language Models—0
Enhanced Infield Agriculture with Interpretable Machine Learning Approaches for Crop Classification—0
Defining Boundaries: The Impact of Domain Specification on Cross-Language and Cross-Domain Transfer in Machine Translation—0
Transfer Learning and the Early Estimation of Single-Photon Source Quality using Machine Learning MethodsCode0
Embedding Ordinality to Binary Loss Function for Improving Solar Flare ForecastingCode0
RedWhale: An Adapted Korean LLM Through Efficient Continual Pretraining—0
Domain-invariant Progressive Knowledge Distillation for UAV-based Object Detection—0
Transfer Operator Learning with Fusion Frame—0
ViLReF: An Expert Knowledge Enabled Vision-Language Retinal Foundation ModelCode1
TDS-CLIP: Temporal Difference Side Network for Image-to-Video Transfer LearningCode1
Multichannel Attention Networks with Ensembled Transfer Learning to Recognize Bangla Handwritten Charecter—0
The Evolution of Reinforcement Learning in Quantitative Finance: A Survey—0
Parameter-Efficient Transfer Learning under Federated Learning for Automatic Speech Recognition—0
Electron-nucleus cross sections from transfer learning—0
Continual Dialogue State Tracking via Reason-of-Select DistillationCode0
TaSL: Continual Dialog State Tracking via Task Skill Localization and ConsolidationCode1
Weakly Supervised Pretraining and Multi-Annotator Supervised Finetuning for Facial Wrinkle Detection—0
Advancing Voice Cloning for Nepali: Leveraging Transfer Learning in a Low-Resource Language—0
Meta-Learning on Augmented Gene Expression Profiles for Enhanced Lung Cancer DetectionCode0
CLIP-CID: Efficient CLIP Distillation via Cluster-Instance Discrimination—0
GitHub is an effective platform for collaborative and reproducible laboratory researchCode1
SA-GDA: Spectral Augmentation for Graph Domain Adaptation—0
Efficient Task Transfer for HLS DSE—0
Computational strategies for cross-species knowledge transfer and translational biomedicine—0
Show:102550
← PrevPage 53 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