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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3276–3300 of 10307 papers

TitleStatusHype
CAKD: A Correlation-Aware Knowledge Distillation Framework Based on Decoupling Kullback-Leibler Divergence—0
Dynamic Indoor Fingerprinting Localization based on Few-Shot Meta-Learning with CSI Images—0
Dynamic Knowledge Distillation for Black-box Hypothesis Transfer Learning—0
Calibrated and Robust Foundation Models for Vision-Language and Medical Image Tasks Under Distribution Shift—0
Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking—0
Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Tensorflow Pretrained Models—0
Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking—0
A Comparison of LSTM and BERT for Small Corpus—0
Dynamic Transfer Learning for Named Entity Recognition—0
Deep learning and high harmonic generation—0
DynaQuant: Compressing Deep Learning Training Checkpoints via Dynamic Quantization—0
CALLIC: Content Adaptive Learning for Lossless Image Compression—0
Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Survey—0
Early detection of diabetes through transfer learning-based eye (vision) screening and improvement of machine learning model performance and advanced parameter setting algorithms—0
Early Diagnosis and Severity Assessment of Weligama Coconut Leaf Wilt Disease and Coconut Caterpillar Infestation using Deep Learning-based Image Processing Techniques—0
Deep Learning and Health Informatics for Smart Monitoring and Diagnosis—0
Early Diagnosis of Parkinsons Disease by Analyzing Magnetic Resonance Imaging Brain Scans and Patient Characteristics—0
Camera On-boarding for Person Re-identification using Hypothesis Transfer Learning—0
Early Prediction of Sepsis: Feature-Aligned Transfer Learning—0
Automatic Plaque Detection in IVOCT Pullbacks Using Convolutional Neural Networks—0
Enhancing learning in spiking neural networks through neuronal heterogeneity and neuromodulatory signaling—0
Deep Learning Analysis of Cardiac MRI in Legacy Datasets: Multi-Ethnic Study of Atherosclerosis—0
ECAT: A Entire space Continual and Adaptive Transfer Learning Framework for Cross-Domain Recommendation—0
ECAvg: An Edge-Cloud Collaborative Learning Approach using Averaged Weights—0
Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ—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