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 3526–3550 of 10307 papers

TitleStatusHype
Enhanced Transfer Learning Through Medical Imaging and Patient Demographic Data Fusion—0
Enhanced Transport Distance for Unsupervised Domain Adaptation—0
Deep Doubly Supervised Transfer Network for Diagnosis of Breast Cancer with Imbalanced Ultrasound Imaging Modalities—0
Enhance Visual Recognition under Adverse Conditions via Deep Networks—0
Enhancing Accuracy in Generative Models via Knowledge Transfer—0
Enhancing Action Recognition from Low-Quality Skeleton Data via Part-Level Knowledge Distillation—0
Enhancing Biomedical Text Summarization and Question-Answering: On the Utility of Domain-Specific Pre-Training—0
Enhancing Blood Flow Assessment in Diffuse Correlation Spectroscopy: A Transfer Learning Approach with Noise Robustness Analysis—0
Claim Detection in Biomedical Twitter Posts—0
Deep Discriminative Fine-Tuning for Cancer Type Classification—0
Enhancing Bronchoscopy Depth Estimation through Synthetic-to-Real Domain Adaptation—0
Enhancing Clinical Information Extraction with Transferred Contextual Embeddings—0
Enhancing Clinically Significant Prostate Cancer Prediction in T2-weighted Images through Transfer Learning from Breast Cancer—0
Automatic detection of passable roads after floods in remote sensed and social media data—0
Enhancing Continuous Domain Adaptation with Multi-Path Transfer Curriculum—0
ClaRet -- A CNN Architecture for Optical Coherence Tomography—0
Enhancing Cross-domain Click-Through Rate Prediction via Explicit Feature Augmentation—0
Enhancing Cross-target Stance Detection with Transferable Semantic-Emotion Knowledge—0
Enhancing CTR Prediction through Sequential Recommendation Pre-training: Introducing the SRP4CTR Framework—0
Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models—0
Class-based Subset Selection for Transfer Learning under Extreme Label Shift—0
Class Conditional Alignment for Partial Domain Adaptation—0
Enhancing ensemble learning and transfer learning in multimodal data analysis by adaptive dimensionality reduction—0
Enhancing Entertainment Translation for Indian Languages using Adaptive Context, Style and LLMs—0
Deep Decomposition for Stochastic Normal-Abnormal Transport—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