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 6001–6025 of 10307 papers

TitleStatusHype
Learning with Less Labels in Digital Pathology via Scribble Supervision from Natural Images—0
A Transfer Learning Pipeline for Educational Resource Discovery with Application in Leading Paragraph Generation—0
Attention Option-Critic—0
Multi-Label Classification on Remote-Sensing Images—0
An exploratory experiment on Hindi, Bengali hate-speech detection and transfer learning using neural networks—0
Probing TryOnGAN—0
Sign Language Recognition System using TensorFlow Object Detection API—0
Stain Normalized Breast Histopathology Image Recognition using Convolutional Neural Networks for Cancer Detection—0
Transfer Learning for Retinal Vascular Disease Detection: A Pilot Study with Diabetic Retinopathy and Retinopathy of Prematurity—0
Network Collaborator: Knowledge Transfer Between Network Reconstruction and Community DetectionCode0
Sentiment Analysis and Sarcasm Detection of Indian General Election Tweets—0
Improving Feature Extraction from Histopathological Images Through A Fine-tuning ImageNet Model—0
Transfer-learning-based Surrogate Model for Thermal Conductivity of Nanofluids—0
Rep-Net: Efficient On-Device Learning via Feature Reprogramming—0
Improving Video Model Transfer With Dynamic Representation Learning—0
A Data-Driven Approach to Improve 3D Head-Pose Estimation—0
Revisiting Learnable Affines for Batch Norm in Few-Shot Transfer Learning—0
SpaceEdit: Learning a Unified Editing Space for Open-Domain Image Color Editing—0
A Theoretical Understanding of Gradient Bias in Meta-Reinforcement LearningCode0
Data-Free Knowledge Transfer: A Survey—0
Representation Learning via Consistent Assignment of Views to ClustersCode0
Transfer learning for cancer diagnosis in histopathological images—0
Transfer learning of phase transitions in percolation and directed percolation—0
Deep Transfer-Learning for patient specific model re-calibration: Application to sEMG-Classification—0
On the Role of Neural Collapse in Transfer Learning—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