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 9251–9275 of 10307 papers

TitleStatusHype
Transfer-Recursive-Ensemble Learning for Multi-Day COVID-19 Prediction in India using Recurrent Neural Networks—0
Synonym Expansion for Large Shopping Taxonomies—0
Random Projections of Mel-Spectrograms as Low-Level Features for Automatic Music Genre Classification—0
Combined Peak Reduction and Self-Consumption Using Proximal Policy Optimization—0
Ranking and Rejecting of Pre-Trained Deep Neural Networks in Transfer Learning based on Separation Index—0
Combined Scaling for Zero-shot Transfer Learning—0
Combinets: Creativity via Recombination of Neural Networks—0
Combining Behaviors with the Successor Features Keyboard—0
Combining Convolution and Recursive Neural Networks for Sentiment Analysis—0
Ranking Neural Checkpoints—0
Combining Deep Transfer Learning with Signal-image Encoding for Multi-Modal Mental Wellbeing Classification—0
Synonyms, Antonyms and Beyond—0
Combining Federated Learning and Control: A Survey—0
Combining General and Personalized Models for Epilepsy Detection with Hyperdimensional Computing—0
Combining human parsing with analytical feature extraction and ranking schemes for high-generalization person reidentification—0
Combining Image Features and Patient Metadata to Enhance Transfer Learning—0
Combining Sequence Distillation and Transfer Learning for Efficient Low-Resource Neural Machine Translation Models—0
Open-Ended Fine-Grained 3D Object Categorization by Combining Shape and Texture Features in Multiple Colorspaces—0
Training Data Independent Image Registration With GANs Using Transfer Learning And Segmentation Information—0
RAP: Efficient Text-Video Retrieval with Sparse-and-Correlated Adapter—0
Combining Weakly Supervised ML Techniques for Low-Resource NLU—0
Come hither or go away? Recognising pre-electoral coalition signals in the news—0
Command-line Risk Classification using Transformer-based Neural Architectures—0
BNS: Building Network Structures Dynamically for Continual Learning—0
BMIKE-53: Investigating Cross-Lingual Knowledge Editing with In-Context Learning—0
Show:102550
← PrevPage 371 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