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 5626–5650 of 10307 papers

TitleStatusHype
Sleep Position Classification using Transfer Learning for Bed-based Pressure Sensors—0
SleepPPG-Net: a deep learning algorithm for robust sleep staging from continuous photoplethysmography—0
SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models—0
Small Object Detection: A Comprehensive Survey on Challenges, Techniques and Real-World Applications—0
Småprat: DialoGPT for Natural Language Generation of Swedish Dialogue by Transfer Learning—0
Smart Augmentation - Learning an Optimal Data Augmentation Strategy—0
Smart City Development with Urban Transfer Learning—0
SmartFPS: Neural Network based Wireless-inertial fusion positioning system—0
Smart Irrigation IoT Solution using Transfer Learning for Neural Networks—0
Smart Knowledge Transfer using Google-like Search—0
Smartphone App Usage Prediction Using Points of Interest—0
Smartphone-Based Test and Predictive Models for Rapid, Non-Invasive, and Point-of-Care Monitoring of Ocular and Cardiovascular Complications Related to Diabetes—0
SMC Faster R-CNN: Toward a scene-specialized multi-object detector—0
SMC-UDA: Structure-Modal Constraint for Unsupervised Cross-Domain Renal Segmentation—0
Smile, Be Happy :) Emoji Embedding for Visual Sentiment Analysis—0
Smile detection in the wild based on transfer learning—0
SMILE: Self-Distilled MIxup for Efficient Transfer LEarning—0
Smoothing Adversarial Domain Attack and P-Memory Reconsolidation for Cross-Domain Person Re-Identification—0
Smoothness Adaptive Hypothesis Transfer Learning—0
sMRI-PatchNet: A novel explainable patch-based deep learning network for Alzheimer's disease diagnosis and discriminative atrophy localisation with Structural MRI—0
SNN: Stacked Neural Networks—0
CrAFT: Compression-Aware Fine-Tuning for Efficient Visual Task Adaptation—0
SOAC: The Soft Option Actor-Critic Architecture—0
SoccerKDNet: A Knowledge Distillation Framework for Action Recognition in Soccer Videos—0
Social IQa: Commonsense Reasoning about Social Interactions—0
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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