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 3351–3375 of 10307 papers

TitleStatusHype
Improving Implicit Feedback-Based Recommendation through Multi-Behavior AlignmentCode1
Application of Artificial Intelligence in the Classification of Microscopical Starch Images for Drug Formulation—0
Bone Marrow Cytomorphology Cell Detection using InceptionResNetV2—0
Detection of depression on social networks using transformers and ensemblesCode0
CrAFT: Compression-Aware Fine-Tuning for Efficient Visual Task Adaptation—0
MultiTACRED: A Multilingual Version of the TAC Relation Extraction DatasetCode1
SEGA: Structural Entropy Guided Anchor View for Graph Contrastive LearningCode0
Model-Contrastive Federated Domain Adaptation—0
Approximation by non-symmetric networks for cross-domain learning—0
PointCMP: Contrastive Mask Prediction for Self-supervised Learning on Point Cloud VideosCode1
Towards a Simple Framework of Skill Transfer Learning for Robotic Ultrasound-guidance ProceduresCode0
Cross-domain Augmentation Networks for Click-Through Rate Prediction—0
Online Gesture Recognition using Transformer and Natural Language Processing—0
Knowledge Transfer from Teachers to Learners in Growing-Batch Reinforcement Learning—0
Towards Effective Collaborative Learning in Long-Tailed Recognition—0
An Imitation Learning Based Algorithm Enabling Priori Knowledge Transfer in Modern Electricity Markets for Bayesian Nash Equilibrium Estimation—0
Semi-supervised Domain Adaptation via Prototype-based Multi-level LearningCode1
Avatar Knowledge Distillation: Self-ensemble Teacher Paradigm with UncertaintyCode1
Emulation Learning for Neuromimetic Systems—0
Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class ChallengeCode0
Shotgun crystal structure prediction using machine-learned formation energiesCode1
DocLangID: Improving Few-Shot Training to Identify the Language of Historical DocumentsCode0
Improving Contrastive Learning of Sentence Embeddings from AI FeedbackCode1
VPGTrans: Transfer Visual Prompt Generator across LLMsCode2
Improving Cancer Hallmark Classification with BERT-based Deep Learning Approach—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