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 651–700 of 10307 papers

TitleStatusHype
Rethinking Membership Inference Attacks Against Transfer Learning—0
Transfer Learning Strategies for Pathological Foundation Models: A Systematic Evaluation in Brain Tumor Classification—0
Enhancing Brain Tumor Segmentation Using Channel Attention and Transfer learningCode0
Adaptive Target Localization under Uncertainty using Multi-Agent Deep Reinforcement Learning with Knowledge Transfer—0
BeST -- A Novel Source Selection Metric for Transfer Learning—0
Model-Robust and Adaptive-Optimal Transfer Learning for Tackling Concept Shifts in Nonparametric Regression—0
A Resource-Efficient Training Framework for Remote Sensing Text--Image RetrievalCode0
Automatic Speech Recognition for Sanskrit with Transfer Learning—0
Surrogate-based multiscale analysis of experiments on thermoplastic composites under off-axis loadingCode1
Sequential PatchCore: Anomaly Detection for Surface Inspection using Synthetic Impurities—0
Teaching Wav2Vec2 the Language of the BrainCode0
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
A Bayesian Hierarchical Model for Generating Synthetic Unbalanced Power Distribution Grids—0
Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models—0
An analysis of data variation and bias in image-based dermatological datasets for machine learning classification—0
Incrementally Learning Multiple Diverse Data Domains via Multi-Source Dynamic Expansion Model—0
Continual Deep Active Learning for Medical Imaging: Replay-Base Architecture for Context AdaptationCode0
Data-driven inventory management for new products: An adjusted Dyna-Q approach with transfer learning—0
Optimal Policy Adaptation under Covariate Shift—0
AgentPose: Progressive Distribution Alignment via Feature Agent for Human Pose Distillation—0
Exploring the Use of Contrastive Language-Image Pre-Training for Human Posture Classification: Insights from Yoga Pose Analysis—0
Dual Scale-aware Adaptive Masked Knowledge Distillation for Object Detection—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
Rice Leaf Disease Detection: A Comparative Study Between CNN, Transformer and Non-neural Network Architectures—0
Transfer Learning of Tabular Data by Finetuning Large Language Models—0
Mathematics of Digital Twins and Transfer Learning for PDE Models—0
Transforming Social Science Research with Transfer Learning: Social Science Survey Data Integration with AI—0
Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot CoordinationCode0
Super-class guided Transformer for Zero-Shot Attribute ClassificationCode1
Load Forecasting for Households and Energy Communities: Are Deep Learning Models Worth the Effort?Code1
A CT Image Classification Network Framework for Lung Tumors Based on Pre-trained MobileNetV2 Model and Transfer learning, And Its Application and Market Analysis in the Medical field—0
AD-L-JEPA: Self-Supervised Spatial World Models with Joint Embedding Predictive Architecture for Autonomous Driving with LiDAR DataCode1
Patch-GAN Transfer Learning with Reconstructive Models for Cloud Removal—0
Knowledge Transfer in Model-Based Reinforcement Learning Agents for Efficient Multi-Task Learning—0
A Text-Based Knowledge-Embedded Soft Sensing Modeling Approach for General Industrial Process Tasks Based on Large Language Model—0
TADFormer : Task-Adaptive Dynamic Transformer for Efficient Multi-Task Learning—0
Comparison of Neural Models for X-ray Image Classification in COVID-19 Detection—0
Deep Transfer Q-Learning for Offline Non-Stationary Reinforcement Learning—0
Rapid Automated Mapping of Clouds on Titan With Instance SegmentationCode0
Cued Speech Generation Leveraging a Pre-trained Audiovisual Text-to-Speech Model—0
A novel Facial Recognition technique with Focusing on Masked Faces—0
Enhancing Scene Classification in Cloudy Image Scenarios: A Collaborative Transfer Method with Information Regulation Mechanism using Optical Cloud-Covered and SAR Remote Sensing ImagesCode0
DeepVIVONet: Using deep neural operators to optimize sensor locations with application to vortex-induced vibrations—0
Transfer Learning for Deep-Unfolded Combinatorial Optimization Solver with Quantum Annealer—0
SelectiveFinetuning: Enhancing Transfer Learning in Sleep Staging through Selective Domain Alignment—0
A Multimodal Lightweight Approach to Fault Diagnosis of Induction Motors in High-Dimensional Dataset—0
Improving Dialectal Slot and Intent Detection with Auxiliary Tasks: A Multi-Dialectal Bavarian Case StudyCode0
A Diffusion Model and Knowledge Distillation Framework for Robust Coral Detection in Complex Underwater EnvironmentsCode0
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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