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 4026–4050 of 10307 papers

TitleStatusHype
Parameter and Computation Efficient Transfer Learning for Vision-Language Pre-trained ModelsCode0
Deep Learning Approach for Large-Scale, Real-Time Quantification of Green Fluorescent Protein-Labeled Biological Samples in Microreactors—0
User lung cancer classification using efficientnet from ct scan images—0
Knowledge Graph Embeddings for Multi-Lingual Structured Representations of Radiology Reports—0
Towards Optimal Patch Size in Vision Transformers for Tumor SegmentationCode0
Exploring Cross-Cultural Differences in English Hate Speech Annotations: From Dataset Construction to AnalysisCode0
Exploring Model Transferability through the Lens of Potential EnergyCode0
Uncovering the Hidden Cost of Model CompressionCode0
Target PCA: Transfer Learning Large Dimensional Panel Data—0
On the Steganographic Capacity of Selected Learning Models—0
Multi-Transfer Learning Techniques for Detecting Auditory Brainstem Response—0
Robust Activity Recognition for Adaptive Worker-Robot Interaction using Transfer Learning—0
LAC: Latent Action Composition for Skeleton-based Action Segmentation—0
Do the Frankenstein, or how to achieve better out-of-distribution performance with manifold mixing model soup—0
PanoSwin: a Pano-style Swin Transformer for Panorama Understanding—0
Attention-Guided Lidar Segmentation and Odometry Using Image-to-Point Cloud Saliency TransferCode0
Comprehensive performance comparison among different types of features in data-driven battery state of health estimation—0
Revolutionizing Disease Diagnosis: A Microservices-Based Architecture for Privacy-Preserving and Efficient IoT Data Analytics Using Federated Learning—0
Mesh-Wise Prediction of Demographic Composition from Satellite Images Using Multi-Head Convolutional Neural Network—0
Enhanced Mortality Prediction In Patients With Subarachnoid Haemorrhage Using A Deep Learning Model Based On The Initial CT Scan—0
Privacy-Enhanced Zero-Shot Learning via Data-Free Knowledge TransferCode0
CEIMVEN: An Approach of Cutting Edge Implementation of Modified Versions of EfficientNet (V1-V2) Architecture for Breast Cancer Detection and Classification from Ultrasound ImagesCode0
Heterogeneous Federated Learning via Personalized Generative Networks—0
An Ensemble Approach to Personalized Real Time Predictive Writing for Experts—0
Ultrafast-and-Ultralight ConvNet-Based Intelligent Monitoring System for Diagnosing Early-Stage Mpox Anytime and Anywhere—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