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 7726–7750 of 10307 papers

TitleStatusHype
COVID-19 therapy target discovery with context-aware literature mining—0
Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News Exposure during COVID-19 Outbreak—0
Beyond H-Divergence: Domain Adaptation Theory With Jensen-Shannon Divergence—0
Multi-label Zero-shot Classification by Learning to Transfer from External Knowledge—0
An Improvement for Capsule Networks using Depthwise Separable Convolution—0
Learning Representations for Axis-Aligned Decision Forests through Input Perturbation—0
Modular Transfer Learning with Transition Mismatch Compensation for Excessive Disturbance Rejection—0
Force myography benchmark data for hand gesture recognition and transfer learningCode0
Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization—0
Practical and sample efficient zero-shot HPO—0
Evaluation of Federated Learning in Phishing Email Detection—0
Reed at SemEval-2020 Task 9: Fine-Tuning and Bag-of-Words Approaches to Code-Mixed Sentiment Analysis—0
Federated Self-Supervised Learning of Multi-Sensor Representations for Embedded Intelligence—0
Real-World Multi-Domain Data Applications for Generalizations to Clinical Settings—0
Dynamic Knowledge Distillation for Black-box Hypothesis Transfer Learning—0
Enhanced Transfer Learning for Autonomous Driving with Systematic Accident Simulation—0
Leveraging Undiagnosed Data for Glaucoma Classification with Teacher-Student LearningCode0
Dog Identification using Soft Biometrics and Neural Networks—0
Multi-task learning for natural language processing in the 2020s: where are we going?—0
Effects of Language Relatedness for Cross-lingual Transfer Learning in Character-Based Language Models—0
Camera On-boarding for Person Re-identification using Hypothesis Transfer Learning—0
A Transfer Learning End-to-End ArabicText-To-Speech (TTS) Deep Architecture—0
XMixup: Efficient Transfer Learning with Auxiliary Samples by Cross-domain Mixup—0
Semi-Supervised Learning Approach to Discover Enterprise User Insights from Feedback and Support—0
Classification of Diabetic Retinopathy via Fundus Photography: Utilization of Deep Learning Approaches to Speed up Disease Detection—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