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 4201–4225 of 10307 papers

TitleStatusHype
Class Conditional Alignment for Partial Domain Adaptation—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
Enhancing CTR Prediction through Sequential Recommendation Pre-training: Introducing the SRP4CTR Framework—0
Enhancing Cross-target Stance Detection with Transferable Semantic-Emotion Knowledge—0
Class-Aware Adversarial Transformers for Medical Image Segmentation—0
Adversarial-Robust Transfer Learning for Medical Imaging via Domain Assimilation—0
Enhancing Cross-domain Click-Through Rate Prediction via Explicit Feature Augmentation—0
ClaRet -- A CNN Architecture for Optical Coherence Tomography—0
Enhancing Continuous Domain Adaptation with Multi-Path Transfer Curriculum—0
Enhancing Cocoa Pod Disease Classification via Transfer Learning and Ensemble Methods: Toward Robust Predictive Modeling—0
Application of Transfer Learning Approaches in Multimodal Wearable Human Activity Recognition—0
Enhancing Clinically Significant Prostate Cancer Prediction in T2-weighted Images through Transfer Learning from Breast Cancer—0
Enhancing Clinical Information Extraction with Transferred Contextual Embeddings—0
Enhancing Bronchoscopy Depth Estimation through Synthetic-to-Real Domain Adaptation—0
Enhancing Breast Cancer Diagnosis in Mammography: Evaluation and Integration of Convolutional Neural Networks and Explainable AI—0
Claim extraction from text using transfer learning.—0
Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology—0
Adversarial Robustness of Discriminative Self-Supervised Learning in Vision—0
Active flow control for three-dimensional cylinders through deep reinforcement learning—0
Enhancing Blood Flow Assessment in Diffuse Correlation Spectroscopy: A Transfer Learning Approach with Noise Robustness Analysis—0
Enhancing Biomedical Text Summarization and Question-Answering: On the Utility of Domain-Specific Pre-Training—0
Enhancing Action Recognition from Low-Quality Skeleton Data via Part-Level Knowledge Distillation—0
Claim Detection in Biomedical Twitter Posts—0
Enhancing Accuracy in Generative Models via Knowledge Transfer—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