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 5251–5300 of 10307 papers

TitleStatusHype
Learning to Transfer—0
Learning to Transfer Dynamic Models of Underactuated Soft Robotic Hands—0
Learning to Transfer for Evolutionary Multitasking—0
Learning to Transfer Graph Embeddings for Inductive Graph based Recommendation—0
Learning to Transfer Learn: Reinforcement Learning-Based Selection for Adaptive Transfer Learning—0
Learning to Transfer: Transferring Latent Task Structures and Its Application to Person-Specific Facial Action Unit Detection—0
Learning to Unlearn: Building Immunity to Dataset Bias in Medical Imaging Studies—0
Learning to Win Lottery Tickets in BERT Transfer via Task-agnostic Mask Training—0
Learning Transferability in Deep Segmentation of Liver Metastases—0
Learning Transferable Conceptual Prototypes for Interpretable Unsupervised Domain Adaptation—0
Land-Cover Classification with High-Resolution Remote Sensing Images Using Transferable Deep Models—0
Learning Transferable Feature Representations Using Neural Networks—0
Learning Transferrable Parameters for Long-tailed Sequential User Behavior Modeling—0
Learning Transfers over Several Programming Languages—0
Learning ULMFiT and Self-Distillation with Calibration for Medical Dialogue System—0
Learning unbiased features—0
Learning under Covariate Shift for Domain Adaptation for Word Sense Disambiguation—0
Learning Universal Policies via Text-Guided Video Generation—0
Learning Unsupervised Word Mapping by Maximizing Mean Discrepancy—0
Learning Unsupervised Word Translations Without Adversaries—0
Learning Visually Consistent Label Embeddings for Zero-Shot Learning—0
Learning with Less Labels in Digital Pathology via Scribble Supervision from Natural Images—0
Learn it or Leave it: Module Composition and Pruning for Continual Learning—0
Learn to Talk via Proactive Knowledge Transfer—0
LEEP: A New Measure to Evaluate Transferability of Learned Representations—0
Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs—0
LegalTurk Optimized BERT for Multi-Label Text Classification and NER—0
LEKA:LLM-Enhanced Knowledge Augmentation—0
LeOCLR: Leveraging Original Images for Contrastive Learning of Visual Representations—0
Less is More: Undertraining Experts Improves Model Upcycling—0
LESS: Large Language Model Enhanced Semi-Supervised Learning for Speech Foundational Models—0
Lesson Learnt: Modularization of Deep Networks Allow Cross-Modality Reuse—0
Lessons from the Use of Natural Language Inference (NLI) in Requirements Engineering Tasks—0
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification—0
Transfer Learning of Real Image Features with Soft Contrastive Loss for Fake Image Detection—0
Let's Focus: Focused Backdoor Attack against Federated Transfer Learning—0
Letter Sequence Labeling for Compound Splitting—0
Leveraging Affect Transfer Learning for Behavior Prediction in an Intelligent Tutoring System—0
Leveraging ASR Pretrained Conformers for Speaker Verification through Transfer Learning and Knowledge Distillation—0
Leveraging Auxiliary Task Relevance for Enhanced Bearing Fault Diagnosis through Curriculum Meta-learning—0
Leveraging Cross-Attention Transformer and Multi-Feature Fusion for Cross-Linguistic Speech Emotion Recognition—0
Leveraging Distillation Techniques for Document Understanding: A Case Study with FLAN-T5—0
Leveraging Foundation Models for Multi-modal Federated Learning with Incomplete Modality—0
Leveraging Industry 4.0 -- Deep Learning, Surrogate Model and Transfer Learning with Uncertainty Quantification Incorporated into Digital Twin for Nuclear System—0
Leveraging Local Domains for Image-to-Image Translation—0
Leveraging Medical Literature for Section Prediction in Electronic Health Records—0
Leveraging Medical Visual Question Answering with Supporting Facts—0
Leveraging Multi-Task Learning for Multi-Label Power System Security Assessment—0
Leveraging Near-Field Lighting for Monocular Depth Estimation from Endoscopy Videos—0
Leveraging neural network interatomic potentials for a foundation model of chemistry—0
Show:102550
← PrevPage 106 of 207Next →
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