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 2076–2100 of 10307 papers

TitleStatusHype
COLA: Cross-city Mobility Transformer for Human Trajectory SimulationCode1
Leveraging Weakly Annotated Data for Hate Speech Detection in Code-Mixed Hinglish: A Feasibility-Driven Transfer Learning Approach with Large Language Models—0
On Latency Predictors for Neural Architecture SearchCode1
A New Perspective on Smiling and Laughter Detection: Intensity Levels Matter—0
Encodings for Prediction-based Neural Architecture SearchCode0
On the impact of measure pre-conditionings on general parametric ML models and transfer learning via domain adaptation—0
Self-Supervised Facial Representation Learning with Facial Region Awareness—0
A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications—0
Is in-domain data beneficial in transfer learning for landmarks detection in x-ray images?—0
Dynamic Adapter Meets Prompt Tuning: Parameter-Efficient Transfer Learning for Point Cloud AnalysisCode2
Knowledge-Reuse Transfer Learning Methods in Molecular and Material Science—0
Transfer Learning-Enhanced Instantaneous Multi-Person Indoor Localization by CSI—0
Fast Low-parameter Video Activity Localization in Collaborative Learning Environments—0
Balancing Exploration and Exploitation in LLM using Soft RLLF for Enhanced Negation Understanding—0
Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey—0
Transfer Learning for Security: Challenges and Future Directions—0
A Regularization-based Transfer Learning Method for Information Extraction via Instructed Graph DecoderCode0
Efficient Adapter Tuning of Pre-trained Speech Models for Automatic Speaker Verification—0
Bias Mitigation in Fine-tuning Pre-trained Models for Enhanced Fairness and Efficiency—0
Cross-Lingual Learning vs. Low-Resource Fine-Tuning: A Case Study with Fact-Checking in TurkishCode0
Generalized User Representations for Transfer Learning—0
Weakly Supervised Monocular 3D Detection with a Single-View Image—0
Effective Two-Stage Knowledge Transfer for Multi-Entity Cross-Domain Recommendation—0
Analysis of the Two-Step Heterogeneous Transfer Learning for Laryngeal Blood Vessel Classification: Issue and Improvement—0
Percept, Chat, and then Adapt: Multimodal Knowledge Transfer of Foundation Models for Open-World Video Recognition—0
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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