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 8051–8075 of 10307 papers

TitleStatusHype
A Hybrid Instance-based Transfer Learning Method—0
SPIRIT: Short-term Prediction of solar IRradIance for zero-shot Transfer learning using Foundation Models—0
A Hybrid End-to-End Spatio-Temporal Attention Neural Network with Graph-Smooth Signals for EEG Emotion Recognition—0
The design and implementation of Language Learning Chatbot with XAI using Ontology and Transfer Learning—0
Spoiler in a Textstack: How Much Can Transformers Help?—0
SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer—0
Natural Language Processing for Electronic Health Records in Scandinavian Languages: Norwegian, Swedish, and Danish—0
Natural Language Processing in Electronic Health Records in Relation to Healthcare Decision-making: A Systematic Review—0
Natural Language Processing Through Transfer Learning: A Case Study on Sentiment Analysis—0
Natural Language Robot Programming: NLP integrated with autonomous robotic grasping—0
Naturalness Evaluation of Natural Language Generation in Task-oriented Dialogues using BERT—0
Naver Labs Europe's Systems for the Document-Level Generation and Translation Task at WNGT 2019—0
SpotPatch: Parameter-Efficient Transfer Learning for Mobile Object Detection—0
Navigating the Future of Federated Recommendation Systems with Foundation Models—0
Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning—0
Nazr-CNN: Fine-Grained Classification of UAV Imagery for Damage Assessment—0
NCART: Neural Classification and Regression Tree for Tabular Data—0
Near-Driven Autonomous Rover Navigation in Complex Environments: Extensions to Urban Search-and-Rescue and Industrial Inspection—0
Near-Field Spot Beamfocusing: A Correlation-Aware Transfer Learning Approach—0
Near-Optimal Linear Regression under Distribution Shift—0
Near real-time map building with multi-class image set labelling and classification of road conditions using convolutional neural networks—0
SpotTheFake: An Initial Report on a New CNN-Enhanced Platform for Counterfeit Goods Detection—0
Negation Detection in Dutch Spoken Human-Computer Conversations—0
The Details Matter: Preventing Class Collapse in Supervised Contrastive Learning—0
NemaNet: A convolutional neural network model for identification of nematodes soybean crop in brazil—0
Show:102550
← PrevPage 323 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