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 1951–1975 of 10307 papers

TitleStatusHype
Asynchronous Personalized Federated Learning through Global Memorization—0
Towards Understanding the Benefit of Multitask Representation Learning in Decision Process—0
Fine-tuning machine-learned particle-flow reconstruction for new detector geometries in future colliders—0
Foundation-Model-Boosted Multimodal Learning for fMRI-based Neuropathic Pain Drug Response PredictionCode0
RuCCoD: Towards Automated ICD Coding in RussianCode0
Optimal Transfer Learning for Missing Not-at-Random Matrix Completion—0
ECLeKTic: a Novel Challenge Set for Evaluation of Cross-Lingual Knowledge Transfer—0
Exploring Open-world Continual Learning with Knowns-Unknowns Knowledge TransferCode0
Deep Convolutional Neural Networks for Palm Fruit Maturity ClassificationCode0
Transfer Learning in Latent Contextual Bandits with Covariate Shift Through Causal TransportabilityCode0
A Sample-Level Evaluation and Generative Framework for Model Inversion AttacksCode0
Can RLHF be More Efficient with Imperfect Reward Models? A Policy Coverage PerspectiveCode0
GraphBridge: Towards Arbitrary Transfer Learning in GNNsCode0
Deep Learning-Based Transfer Learning for Classification of Cassava Disease—0
Conformal Prediction Under Generalized Covariate Shift with Posterior Drift—0
Transfer Learning for Transient Classification: From Simulations to Real Data and ZTF to LSST—0
Language Models' Factuality Depends on the Language of InquiryCode0
Geometric Properties and Graph-Based Optimization of Neural Networks: Addressing Non-Linearity, Dimensionality, and Scalability—0
Provable Benefits of Unsupervised Pre-training and Transfer Learning via Single-Index Models—0
Multimodal Bearing Fault Classification Under Variable Conditions: A 1D CNN with Transfer Learning—0
Diagnosing COVID-19 Severity from Chest X-Ray Images Using ViT and CNN ArchitecturesCode0
Rewards-based image analysis in microscopy—0
AUKT: Adaptive Uncertainty-Guided Knowledge Transfer with Conformal Prediction—0
Iterative Auto-Annotation for Scientific Named Entity Recognition Using BERT-Based Models—0
Separated Contrastive Learning for Matching in Cross-domain Recommendation with Curriculum Scheduling—0
Show:102550
← PrevPage 79 of 413Next →
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