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 9276–9300 of 10307 papers

TitleStatusHype
CommonCanvas: Open Diffusion Models Trained on Creative-Commons Images—0
Rapid aerodynamic prediction of swept wings via physics-embedded transfer learning—0
Commonsense Knowledge Transfer for Pre-trained Language Models—0
Commonsense Knowledge Transfer for Pre-trained Language Models—0
Common Spatial Generative Adversarial Networks based EEG Data Augmentation for Cross-Subject Brain-Computer Interface—0
Block Toeplitz Sparse Precision Matrix Estimation for Large-Scale Interval-Valued Time Series Forecasting—0
Blockchain as an Enabler for Transfer Learning in Smart Environments—0
Community-based Multi-Agent Reinforcement Learning with Transfer and Active Exploration—0
Comparative Analysis of Deep Learning Models for Crop Disease Detection: A Transfer Learning Approach—0
Syntactically Meaningful and Transferable Recursive Neural Networks for Aspect and Opinion Extraction—0
Comparative Analysis of Imbalanced Malware Byteplot Image Classification using Transfer Learning—0
Comparative Analysis of Lightweight Deep Learning Models for Memory-Constrained Devices—0
Comparative Analysis of Modality Fusion Approaches for Audio-Visual Person Identification and Verification—0
Rapid Classification of Glaucomatous Fundus Images—0
3DPalsyNet: A Facial Palsy Grading and Motion Recognition Framework using Fully 3D Convolutional Neural Networks—0
Comparative Analysis of Transfer Learning in Deep Learning Text-to-Speech Models on a Few-Shot, Low-Resource, Customized Dataset—0
Rapidly and accurately estimating brain strain and strain rate across head impact types with transfer learning and data fusion—0
Syntax-based Transfer Learning for the Task of Biomedical Relation Extraction—0
Blessing of Class Diversity in Pre-training—0
Comparative Evaluation of Transfer Learning for Classification of Brain Tumor Using MRI—0
Rapid Speaker Adaptation in Low Resource Text to Speech Systems using Synthetic Data and Transfer learning—0
Comparing Male Nyala and Male Kudu Classification using Transfer Learning with ResNet-50 and VGG-16—0
Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark—0
Comparing Unsupervised Word Translation Methods Step by Step—0
Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification—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