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 1726–1750 of 10307 papers

TitleStatusHype
The Application of Deep Learning for Lymph Node Segmentation: A Systematic Review—0
Improved Brain Tumor Detection in MRI: Fuzzy Sigmoid Convolution in Deep Learning—0
VaCDA: Variational Contrastive Alignment-based Scalable Human Activity Recognition—0
Structural Alignment in Link PredictionCode0
Prompted Meta-Learning for Few-shot Knowledge Graph Completion—0
HMAE: Self-Supervised Few-Shot Learning for Quantum Spin Systems—0
Multi-modal cascade feature transfer for polymer property prediction—0
Sustainable Smart Farm Networks: Enhancing Resilience and Efficiency with Decision Theory-Guided Deep Reinforcement Learning—0
Comparative Analysis of Lightweight Deep Learning Models for Memory-Constrained Devices—0
Aerodynamic and structural airfoil shape optimisation via Transfer Learning-enhanced Deep Reinforcement Learning—0
Advanced Clustering Framework for Semiconductor Image Analytics Integrating Deep TDA with Self-Supervised and Transfer Learning Techniques—0
Early Prediction of Sepsis: Feature-Aligned Transfer Learning—0
Finger Pose Estimation for Under-screen Fingerprint SensorCode0
Local Herb Identification Using Transfer Learning: A CNN-Powered Mobile Application for Nepalese Flora—0
Low-Complexity Acoustic Scene Classification with Device Information in the DCASE 2025 ChallengeCode0
Transfer Learning-Based Deep Residual Learning for Speech Recognition in Clean and Noisy Environments—0
A Physics-preserved Transfer Learning Method for Differential Equations—0
GENMO: A GENeralist Model for Human MOtion—0
A Computational Model of Inclusive Pedagogy: From Understanding to Application—0
A Robust Deep Networks based Multi-Object MultiCamera Tracking System for City Scale Traffic—0
Explorative Curriculum Learning for Strongly Correlated Electron Systems—0
Uncertainty-Aware Multi-Expert Knowledge Distillation for Imbalanced Disease Grading—0
AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality—0
Camouflaged Variational Graph AutoEncoder against Attribute Inference Attacks for Cross-Domain Recommendation—0
CAE-DFKD: Bridging the Transferability Gap in Data-Free Knowledge Distillation—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