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 8901–8925 of 10307 papers

TitleStatusHype
BERT-Based Approach for Automating Course Articulation Matrix Construction with Explainable AICode0
BERT for Sentiment Analysis: Pre-trained and Fine-Tuned AlternativesCode0
BERT is Not an Interlingua and the Bias of TokenizationCode0
BERT WEAVER: Using WEight AVERaging to enable lifelong learning for transformer-based models in biomedical semantic search enginesCode0
Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog ModelCode0
Best Practices for Large-Scale, Pixel-Wise Crop Mapping and Transfer Learning WorkflowsCode0
Beyond English: Evaluating Automated Measurement of Moral Foundations in Non-English Discourse with a Chinese Case StudyCode0
Learning representations that are closed-form Monge mapping optimal with application to domain adaptationCode0
Beyond Knowledge Silos: Task Fingerprinting for Democratization of Medical Imaging AICode0
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning ArchitectureCode0
Beyond Shared Vocabulary: Increasing Representational Word Similarities across Languages for Multilingual Machine TranslationCode0
Bilateral Personalized Dialogue Generation with Contrastive LearningCode0
Bilingual Alignment Pre-Training for Zero-Shot Cross-Lingual TransferCode0
BioADAPT-MRC: Adversarial Learning-based Domain Adaptation Improves Biomedical Machine Reading Comprehension TaskCode0
Biologically Plausible Training Mechanisms for Self-Supervised Learning in Deep NetworksCode0
Bird Species Classification using Transfer Learning with Multistage TrainingCode0
Black-box Safety Analysis and Retraining of DNNs based on Feature Extraction and ClusteringCode0
Blending-target Domain Adaptation by Adversarial Meta-Adaptation NetworksCode0
BLIP-Adapter: Parameter-Efficient Transfer Learning for Mobile Screenshot CaptioningCode0
Anti-Transfer Learning for Task Invariance in Convolutional Neural Networks for Speech ProcessingCode0
Bone Fracture Classification using Transfer LearningCode0
Boosting Data Analytics With Synthetic Volume ExpansionCode0
Boosting Handwriting Text Recognition in Small Databases with Transfer LearningCode0
Boosting High Resolution Image Classification with Scaling-up TransformersCode0
Bootstrapping the Performance of Webly Supervised Semantic SegmentationCode0
Show:102550
← PrevPage 357 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