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

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Showing 651–675 of 10307 papers

TitleStatusHype
Rethinking Membership Inference Attacks Against Transfer Learning—0
BeST -- A Novel Source Selection Metric for Transfer Learning—0
Enhancing Brain Tumor Segmentation Using Channel Attention and Transfer learningCode0
Transfer Learning Strategies for Pathological Foundation Models: A Systematic Evaluation in Brain Tumor Classification—0
Adaptive Target Localization under Uncertainty using Multi-Agent Deep Reinforcement Learning with Knowledge Transfer—0
A Resource-Efficient Training Framework for Remote Sensing Text--Image RetrievalCode0
Model-Robust and Adaptive-Optimal Transfer Learning for Tackling Concept Shifts in Nonparametric Regression—0
Surrogate-based multiscale analysis of experiments on thermoplastic composites under off-axis loadingCode1
Automatic Speech Recognition for Sanskrit with Transfer Learning—0
Sequential PatchCore: Anomaly Detection for Surface Inspection using Synthetic Impurities—0
Teaching Wav2Vec2 the Language of the BrainCode0
Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models—0
A Bayesian Hierarchical Model for Generating Synthetic Unbalanced Power Distribution Grids—0
Empowering Agricultural Insights: RiceLeafBD - A Novel Dataset and Optimal Model Selection for Rice Leaf Disease Diagnosis through Transfer Learning Technique—0
Densely Connected Parameter-Efficient Tuning for Referring Image SegmentationCode2
Incrementally Learning Multiple Diverse Data Domains via Multi-Source Dynamic Expansion Model—0
An analysis of data variation and bias in image-based dermatological datasets for machine learning classification—0
Data-driven inventory management for new products: An adjusted Dyna-Q approach with transfer learning—0
Continual Deep Active Learning for Medical Imaging: Replay-Base Architecture for Context AdaptationCode0
Optimal Policy Adaptation under Covariate Shift—0
AgentPose: Progressive Distribution Alignment via Feature Agent for Human Pose Distillation—0
Dual Scale-aware Adaptive Masked Knowledge Distillation for Object Detection—0
Exploring the Use of Contrastive Language-Image Pre-Training for Human Posture Classification: Insights from Yoga Pose Analysis—0
AlgoRxplorers | Precision in Mutation: Enhancing Drug Design with Advanced Protein Stability Prediction Tools—0
Robust Hybrid Classical-Quantum Transfer Learning Model for Text Classification Using GPT-Neo 125M with LoRA & SMOTE EnhancementCode0
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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