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 6876–6900 of 10307 papers

TitleStatusHype
Similarity of Pre-trained and Fine-tuned Representations—0
Simple and Effective Transfer Learning for Neuro-Symbolic Integration—0
Boosting Kidney Stone Identification in Endoscopic Images Using Two-Step Transfer Learning—0
Simple Control Baselines for Evaluating Transfer Learning—0
SimpleMTOD: A Simple Language Model for Multimodal Task-Oriented Dialogue with Symbolic Scene Representation—0
Simple Semantic Annotation and Situation Frames: Two Approaches to Basic Text Understanding in LORELEI—0
Boosting Low-Resource Biomedical QA via Entity-Aware Masking Strategies—0
Beyond Fine-tuning: Few-Sample Sentence Embedding Transfer—0
Simple yet Effective Code-Switching Language Identification with Multitask Pre-Training and Transfer Learning—0
Boosting multi-demographic federated learning for chest x-ray analysis using general-purpose self-supervised representations—0
Sim-to-Real Optimization of Complex Real World Mobile Network with Imperfect Information via Deep Reinforcement Learning from Self-play—0
Sim-to-Real Transfer in Multi-agent Reinforcement Networking for Federated Edge Computing—0
Sim-to-Real Transfer Learning using Robustified Controllers in Robotic Tasks involving Complex Dynamics—0
Boosting offline handwritten text recognition in historical documents with few labeled lines—0
Simultaneously Evolving Deep Reinforcement Learning Models using Multifactorial Optimization—0
SINAI at SemEval-2020 Task 12: Offensive Language Identification Exploring Transfer Learning Models—0
SINAI-DL at SemEval-2019 Task 7: Data Augmentation and Temporal Expressions—0
Boosting pathology detection in infants by deep transfer learning from adult speech—0
SingIt! Singer Voice Transformation—0
Single Image Action Recognition by Predicting Space-Time Saliency—0
Boosting Personalised Musculoskeletal Modelling with Physics-informed Knowledge Transfer—0
Size doesn't matter: predicting physico- or biochemical properties based on dozens of molecules—0
Size Independent Neural Transfer for RDDL Planning—0
Boosting Self-Supervised Learning via Knowledge Transfer—0
SkeleTR: Towards Skeleton-based Action Recognition in the Wild—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