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 7151–7175 of 10307 papers

TitleStatusHype
Leveraging Medical Literature for Section Prediction in Electronic Health Records—0
A New Multiple Source Domain Adaptation Fault Diagnosis Method between Different Rotating Machines—0
Leveraging Medical Visual Question Answering with Supporting Facts—0
Leveraging Multi-Task Learning for Multi-Label Power System Security Assessment—0
SingIt! Singer Voice Transformation—0
Leveraging Near-Field Lighting for Monocular Depth Estimation from Endoscopy Videos—0
Leveraging neural network interatomic potentials for a foundation model of chemistry—0
Leveraging Non-Conversational Tasks for Low Resource Slot Filling: Does it help?—0
Leveraging Parameter-Efficient Transfer Learning for Multi-Lingual Text-to-Speech Adaptation—0
A new method using deep transfer learning on ECG to predict the response to cardiac resynchronization therapy—0
Leveraging Pre-trained AudioLDM for Sound Generation: A Benchmark Study—0
On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift—0
Leveraging Road Area Semantic Segmentation with Auxiliary Steering Task—0
A New Method for Vehicle Logo Recognition Based on Swin Transformer—0
Leveraging Seq2seq Language Generation for Multi-level Product Issue Identification—0
A New Mask R-CNN Based Method for Improved Landslide Detection—0
A New Large Scale Dynamic Texture Dataset with Application to ConvNet Understanding—0
Leveraging Speech PTM, Text LLM, and Emotional TTS for Speech Emotion Recognition—0
A New GAN-based End-to-End TTS Training Algorithm—0
Leveraging Text Data Using Hybrid Transformer-LSTM Based End-to-End ASR in Transfer Learning—0
Single Image Action Recognition by Predicting Space-Time Saliency—0
Leveraging Transfer Learning and User-Specific Updates for Rapid Training of BCI Decoders—0
A New Deep Boosted CNN and Ensemble Learning based IoT Malware Detection—0
Segmentation of Skin Lesions and their Attributes Using Multi-Scale Convolutional Neural Networks and Domain Specific Augmentations—0
Leveraging Transformers for StarCraft Macromanagement Prediction—0
Show:102550
← PrevPage 287 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