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 6101–6125 of 10307 papers

TitleStatusHype
Functionally Regionalized Knowledge Transfer for Low-resource Drug Discovery—0
A Mathematical Framework for Quantifying Transferability in Multi-source Transfer Learning—0
On Plasticity, Invariance, and Mutually Frozen Weights in Sequential Task Learning—0
Did You Enjoy the Last Supper? An Experimental Study on Cross-Domain NER Models for the Art DomainCode0
Low-Fidelity Video Encoder Optimization for Temporal Action Localization—0
Bypassing Optimization Complexity through Transfer Learning & Deep Neural Nets for Speech Intelligibility Improvement—0
Quick, get me a Dr. BERT: Automatic Grading of Evidence using Transfer LearningCode0
BNS: Building Network Structures Dynamically for Continual Learning—0
Meta Arcade: A Configurable Environment Suite for Meta-Learning—0
A transfer learning-based deep learning approach for automated COVID-19 diagnosis with audio dataCode0
Subtask-dominated Transfer Learning for Long-tail Person Search—0
FinRead: A Transfer Learning Based Tool to Assess Readability of Definitions of Financial Terms—0
SpaceEdit: Learning a Unified Editing Space for Open-Domain Image Editing—0
MC-SSL0.0: Towards Multi-Concept Self-Supervised Learning—0
Beyond Flatland: Pre-training with a Strong 3D Inductive Bias—0
MAPLE: Microprocessor A Priori for Latency Estimation—0
MD-inferred neural network monoclinic finite-strain hyperelasticity models for β-HMX: Sobolev training and validation against physical constraints—0
Deep Decomposition for Stochastic Normal-Abnormal Transport—0
Speech Tasks Relevant to Sleepiness Determined with Deep Transfer Learning—0
Enhanced Transfer Learning Through Medical Imaging and Patient Demographic Data Fusion—0
Conceptually Diverse Base Model Selection for Meta-Learners in Concept Drifting Data StreamsCode0
Buildings Classification using Very High Resolution Satellite Imagery—0
Learning Physical Concepts in Cyber-Physical Systems: A Case StudyCode0
On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources—0
A multitask transfer learning framework for the prediction of virus-human protein-protein interactions—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