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 2726–2750 of 10307 papers

TitleStatusHype
Talking Models: Distill Pre-trained Knowledge to Downstream Models via Interactive Communication—0
Comparative Analysis of Imbalanced Malware Byteplot Image Classification using Transfer Learning—0
Hybrid Inception Architecture with Residual Connection: Fine-tuned Inception-ResNet Deep Learning Model for Lung Inflammation Diagnosis from Chest Radiographs—0
Reducing Intraspecies and Interspecies Covariate Shift in Traumatic Brain Injury EEG of Humans and Mice Using Transfer Euclidean Alignment—0
Graph Neural Network-based EEG Classification: A Survey—0
PAD-Phys: Exploiting Physiology for Presentation Attack Detection in Face Biometrics—0
An evaluation of pre-trained models for feature extraction in image classification—0
MarineDet: Towards Open-Marine Object Detection—0
Towards Distribution-Agnostic Generalized Category DiscoveryCode1
A Theoretical Analysis of the Test Error of Finite-Rank Kernel Ridge Regression—0
Diagnosis and Prognosis of Faults in High-Speed Aeronautical Bearings with a Collaborative Selection Incremental Deep Transfer Learning ApproachCode0
ScaLearn: Simple and Highly Parameter-Efficient Task Transfer by Learning to ScaleCode0
Data-Efficient Strategies for Probabilistic Voltage Envelopes under Network Contingencies—0
A Brief History of Prompt: Leveraging Language Models. (Through Advanced Prompting)—0
An easy zero-shot learning combination: Texture Sensitive Semantic Segmentation IceHrNet and Advanced Style Transfer Learning StrategyCode0
Glioma subtype classification from histopathological images using in-domain and out-of-domain transfer learning: An experimental study—0
Promoting Generalized Cross-lingual Question Answering in Few-resource Scenarios via Self-knowledge DistillationCode0
AI ensemble for signal detection of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergersCode0
A Survey of Incremental Transfer Learning: Combining Peer-to-Peer Federated Learning and Domain Incremental Learning for Multicenter CollaborationCode0
XVO: Generalized Visual Odometry via Cross-Modal Self-Training—0
Mixup Your Own PairsCode1
Hierarchical Cross-Modality Knowledge Transfer with Sinkhorn Attention for CTC-based ASR—0
E2Net: Resource-Efficient Continual Learning with Elastic Expansion NetworkCode0
Nondestructive chicken egg fertility detection using CNN-transfer learning algorithms—0
Transfer Learning for Bayesian Optimization on Heterogeneous Search Spaces—0
Show:102550
← PrevPage 110 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