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 5401–5425 of 10307 papers

TitleStatusHype
Scalable Forward-Forward Algorithm—0
Scalable Greedy Algorithms for Transfer Learning—0
Scalable handwritten text recognition system for lexicographic sources of under-resourced languages and alphabets—0
Scalable Hyperparameter Transfer Learning—0
Scalable Learning of Segment-Level Traffic Congestion Functions—0
Scalable Multi-Task Gaussian Processes with Neural Embedding of Coregionalization—0
Scalable Multi-Task Transfer Learning for Molecular Property Prediction—0
Scalable Neural Data Server: A Data Recommender for Transfer Learning—0
Scalable Transfer Learning with Expert Models—0
Scalable Weight Reparametrization for Efficient Transfer Learning—0
Scalarization for Multi-Task and Multi-Domain Learning at Scale—0
ScaleKD: Distilling Scale-Aware Knowledge in Small Object Detector—0
Scaling Language Model Size in Cross-Device Federated Learning—0
Scaling Law of Sim2Real Transfer Learning in Expanding Computational Materials Databases for Real-World Predictions—0
Scaling Laws for Data-Efficient Visual Transfer Learning—0
Scaling Laws for Downstream Task Performance of Large Language Models—0
Scaling Laws for Transfer—0
Scattering Vision Transformer: Spectral Mixing Matters—0
SCD-Net: Spatiotemporal Clues Disentanglement Network for Self-supervised Skeleton-based Action Recognition—0
Scene-adaptive and Region-aware Multi-modal Prompt for Open Vocabulary Object Detection—0
Scenes-Objects-Actions: A Multi-Task, Multi-Label Video Dataset—0
Schrödinger's Tree -- On Syntax and Neural Language Models—0
SciANN: A Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks—0
Scientific Keyphrase Identification and Classification by Pre-Trained Language Models Intermediate Task Transfer Learning—0
SciWING– A Software Toolkit for Scientific Document Processing—0
Show:102550
← PrevPage 217 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